Masculinity in “A View from the Bridge” Essay

Arthur Miller wrote this play in 1955. He has written many other plays including All My Sons, which was a success at Broadway. Miller was born in 1915, in New York City, but both his parent had emigrated to the US. This play revolves around emigration, so Miller has had a lot of personal experience. This play is based in the late 1940’s, just after the Second World War, when many people were emigrating to the US, looking for a better life. In the play, which is located in Brooklyn, which is a community full of dockworkers, we have a picture of Eddie, Beatrice and Catherine’s lives when Rodolfo and Marco illegally emigrate to the US from Italy. When Catherine (Eddie’s niece) falls in love with Rodolfo, Eddie loses his cool, and reports Marco and Rodolfo to immigration. The drama ends with the death of Eddie, as he tried to kill Marco; Marco turned the knife, and stabbed Eddie. There are three leading male characters in the play; Eddie, Rodolfo and Marco, and each of them play different roles and have different types of roles. The first of these men we meet is Eddie. The first impression of any character is very important, and the first impression we get of Eddie is that he is a real family person. He talks very complimentarily towards Catherine, â€Å"Beautiful! Turn, around, lemme see in the back. Oh if your mother was alive to see you now! She wouldn’t believe it.† This gives the reader the impression that he is a family person, who is close to his niece. Then when he hears that Beatrice (his wife) is cousins have arrived he tells her â€Å"Don’t worry about it B., there’s nothin’ to it. Couple of hours and they’ll be here.† This gives us the impression that he is also very caring towards B’s family, even though they come in illegally. This also enhances the impression that he is a family man. We are made to believe generally that he’s a good man, who values his family very high. When we hear that Catherine has got a job, she wanted to ask Eddie if it was all right with him if she took it. This shows us that he has a lot of status in the house. If he hadn’t any status, Catherine would have gone  behind his back, and wouldn’t have asked permission. Eddie then replied by saying â€Å"Sure she’s the best.† This shows us that he cares for his family and wants the best for his niece. A little later we hear from a lawyer called Alfieri. We are believed to trust him because of his wisdom, and position in the community. He is the voice of the community. He then tells his thoughts towards Eddie. â€Å"He was a good man as he had to be in a life that was hard and even.† The important word here is â€Å"was†, this shows us that Eddie’s personality is on the verge of changing. When we are introduced to Rodolfo and Marco, there is a huge difference between them, first there is their appearance. The impression that we get of Marco is that he’s a strong man, and is focused, because Miller describes him as â€Å"Square-built peasant of thirty-two, suspicious, tender and quiet voiced.† when he is first introduced. This gives us the impression that he is a very quiet person, but his awareness is very good, he is very alert of what’s around him. During the first scene where he’s introduced, when he talks it’s usually very short answers, e.g. â€Å"Thank you† and â€Å"Are you my cousin?† On the other hand, Rodolfo’s manliness is totally different to Eddie’s and Marco’s. Rodolfo is an extremely attractive young man, who is very sensitive. Where Eddie and Marco are much more macho than Rodolfo, unlike Eddie and Marco, Rodolfo sees sewing, cooking and singing as manly. We are made to believe that Rodolfo is quiet intelligent, because the language he uses is very flowery, â€Å"The horses in our town are skinnier than goats.† One of the most notable features that Rodolfo has is his â€Å"so blond† hair. Immediately Eddie goes on the defence, and say’s that is hair is like a â€Å"chorus girl or sump’m.† Then Eddie goes on to imply that he dyes his hair, which in Eddie’s eyes is unacceptable. Without ever saying it, Eddie’s implying that Rodolfo is gay. Another factor that goes against Rodolfo is that he is a very keen singer, and we hear his version of â€Å"Paper Doll†, and in Eddie’s eyes, only homosexuals sing. Due to the difference in physique between both characters, it was inevitable that they wouldn’t be able to work effectively. Marco is full of muscle, so he is adapted very well to work in a shipyard, but because of his slight physique, Rodolfo is not as well adapted. In my view, he would have been much better in the entertainment business, because he is a natural joke teller. However, Eddie tells Rodolfo that â€Å"But as long as you owe them money, they’ll get you plenty of work† that tells us that there are plenty of work in the docks, and Rodolfo is very clever and takes the work just to get some money in. This shows us that Rodolfo has a good brain, and is more concerned so he can get some money in to establish himself as an American. After coming home from work, Eddie goes on the attack, and undermines Rodolfo’s work rate, because he hasn’t taken at all to Rodolfo, He doesn’t see things like sewing, cooking and singing as manly. First of all he complains that â€Å"he sings.† Eddie is very embarrassed of this, because many of his friend e.g. Louis work there, and knowing that Eddie gives a roof to the â€Å"Canary† might under mind his street cred, because it’s like Rodolfo’s giving out â€Å"regular free shows†. Even at home Rodolfo sings, and this really goes under Eddie’s skin, as he says â€Å"if you came in the house and you didn’t †¦ know who was singin’, you wouldn’t be lookin for him you be lookin’ for her.† This shows us that Rodolfo isn’t shy about singing. He wants to make the most of his magical voice. Nevertheless, Eddie realises if someone was to come to the house, to ask, â€Å"who was singing?† he would be extremely embarrassed to say it was a man’s voice. This is extremely ironic, because most of the famous Italian singers are tenors. Although by now a man singing tenor is totally acceptable, back in the time that this play was written, people were less sophisticated, and in my opinion much more prepared to stereotype people. Although Eddie isn’t the biggest fan of Rodolfo, we learn at the end of the first act that Rodolfo is the kind of man that Beatrice and Catherine are looking for. When Marco says that â€Å"everybody gets fat† when Rodolfo’s cooking, Eddie tries to make this count against Rodolfo, but the girls see  through this. Catherine then goes on to glorify the fact that he could cook, and say’s that â€Å"all the big hotels (chefs) are men†. This shows that she thinks that there’s different ways to be a man, either through cooking or dancing. Straight afterwards, in my opinion Eddie feels very vulnerable because two men have arrived, and he’s afraid he would be toppled as king of the castle. So Eddie goes out to win some honour back, and tries to humiliate Rodolfo and Marco. His first target was Rodolfo. He went for one of Rodolfo’s weaknesses, in his opinion his manliness; he isn’t strong enough in Eddie’s view so he decides to teach Rodolfo how to box. Eddie encouraged Rodolfo to â€Å"put sump’m behind it, you can’t hurt me.† and â€Å"Come on show me! What’re you gonna be? Show me!† In my view, Eddie is trying to show that he’s a better and stronger man than Rodolfo; he wants to prove to Beatrice and Catherine, that Rodolfo isn’t the man they think he is. Just to rub the salt into the wound, Eddie â€Å"feints with his right and lands with his right.† Afterwards he asks Rodolfo â€Å"Did I hurt you?† In my opinion, Eddie is waiting for Rodolfo to reply â€Å"Yes†, so Catherine and Beatrice sees such a weak person he is, but Rodolfo replies â€Å"No, no.† This shows the toughness that belongs to Rodolfo that we haven’t seen before in the play. This shows the reader that Eddie hasn’t succeeded in humiliating Rodolfo, but rather he succeeded to humiliate himself. Just to make the situation worse for Eddie, Rodolfo and Catherine continue with their lives and go to dance, they didn’t dwindle on the situation. After seeing his younger brother being treated so horrid by Eddie, Marco decides to challenge Eddie’s masculinity, and bring him back down to earth with a bang. All Marco asked is â€Å"can you lift this chair?† It sounds like a pretty easy thing to do. When Eddie went down on his knees to pick it up, he fails. â€Å"He tries again, and again fails.† Then when Marco goes down to pick it up, he â€Å"raises the chair over his head.† He raised the chair as it was a weapon, and as a word of warning to Eddie. This shows us that Marco is looking after his close family, and wants to make sure that nobody gets the better of them. This lift was more of a warning to Eddie not to mess with Rodolfo, than anything else in my opinion. He did this as his felt  quite a strong responsibility towards Rodolfo. This is a clear sign that Marco is looking for justice, and he isn’t as quiet a character as Miller first portrays him. This is a clear similarity between Marco and Eddie, because both want to protect their families. Although Mike does portray him as a â€Å"regular bull†, that shows that Marco’s strength has been seen through out the community. Catherine is extremely important to the whole plot of the drama, because it’s because of her that the entire feud between Eddie and Rodolfo has erupted. At the beginning of the play we get the idea that Eddie’s extremely protective towards Catherine because he says â€Å"I promised your mother in her deathbed. I’m responsible for you.† At this point we get the idea that Eddie’s like any other caring uncle, but as the drama unfolds, we are made to think that Eddie’s is becoming overly attached to Catherine. When Eddie learns that Rodolfo has extremely strong feelings towards Catherine, he quickly tries to distance Catherine away from him, by saying that â€Å"He don’t respect you.† This is a cry of a desperate man, it’s as if he doesn’t want her to grow up, this is a very strong weakness of Eddies. We learn earlier on in the play that Eddie isn’t a good husband, because Beatrice asks, â€Å"When am I gonna be a wife again, Eddie?† I think that Eddie is confused with the state of his relationship with Catherine as she’s growing up, and because of this it’s stopping him from completing his duties with Beatrice. When Rodolfo sing or dances with Catherine, the song â€Å"Paper Doll† is often used e.g. When Rodolfo tells Catherine to â€Å"Dance† the phonograph â€Å"plays ‘Paper Doll’†. In my opinion this is a very clever use of song because it describes the nature of Rodolfo, he like a news paper. First of all Rodolfo isn’t extremely strong, nor is paper. One of the similarities between Marco and Eddie’s that they’re both very strong. Also you can read Rodolfo’s thoughts by looking at his face, just as if you’re reading a newspaper. One of the differences between Eddie and Marco is that Marco cares for his wife. In my opinion, to be a good man you must look after your wife. Where Eddie forgets to do his duties in bed, Marco sends some of the money he has  won back to his family in Italy, so they can have a better life. As I’ve said, both men want to look after their families, but both do this is different ways. Marco is prepared to leave his family to earn money, whereas Eddie hangs on to his family too tight in my opinion. One of the turning points is the drama, is when Eddie goes around kissing everybody. When Eddie sees Rodolfo and Catherine together, Eddie â€Å"suddenly, draws her to him, and as she strives to free herself he kisses her on the lips.† This is Eddie getting what he wants, that is Catherine, because we know that Eddie doesn’t want Beatrice. Although Eddie gets Catherine in a very brutal way, it shows his dominance in the house. Not just content with this, â€Å"Eddie pins (Rodolfo’s) arms, laughing, and suddenly kisses him.† This is a rather odd gesture, because many times during the play Eddie describes Rodolfo as â€Å"The guy ain’t right.† He uses it many times either because he feels that this is true, or even because he is trying to convince himself that this is right. This kiss doesn’t follow with Eddie’s behaviour during the rest of the play, because by kissing him, he brings himself down to the same masculinity as gays. During the end we learn a lot about the characters real thoughts and feelings, and what sort of men they really are. There is a lot of discussion during the play, asking is Rodolfo just looking after himself, by wanting to marry an American. This is thrown more into doubt when Catherine asks him telling him â€Å"Suppose I wanted to live in Italy. At first he tries to push away the idea by replying â€Å"Forever?† At this point you start to believe that he’s a selfish little Italian that just wants to be an American. But then he goes on and says that â€Å"there’s nothing†¦I would be a criminal stealing your face†. This tells us that he’s as caring as Marco, he only wants the best for Catherine, and that all of Eddie’s doubts seem wrong. As the plot unfolds, Eddie’s masculinity seems to grow weaker and weaker. He has finally cracked when he â€Å"wants to report something†. Illegal immigrants. Two of them.† This shows us that Eddie has finally gone for  the big one. He isn’t enough of a man to throw Rodolpho and Marco on to the street; he phones to get others to do his dirty work. This is a sign of a coward. Although he thinks it’s the right thing to do, because he is protecting Catherine. After two officials catch the illegal immigrants, â€Å"Marco suddenly breaks from the group and dashes into the room and faces Eddie†. This shows us that Marco is a growing threat during this play. He’s becoming more and more important as the plot unfolds. By standing up to Eddie it shows that he’s ready to match him. But instead of attacking him verbally or physically, he â€Å"spits into Eddie’s face†. This is the point where Eddie loses all his dignity and manliness. The Italian community in Brooklyn is extremely close together, and they watch out for each other, and having one of their own betraying them is a sin, so Eddie will be looked down at now by the rest of the community. Not even Louis, one of Eddie’s close friends turn around to look at him when Eddie shouts, â€Å"Louis! Louis!† Even Catherine his own niece says that â€Å"he bites people when they sleep!† This shows us now that nobody will ever be able to trust him again, not even his own family. Just to rub the salt into the wound, Marco shouts, â€Å"That one! He killed my children! That one stole the food from my children!† This shows that the relationship between Eddie and Marco has hit an all time low. This also throws away the scraps of dignity that Eddie had left. It also enhances the fact that Marco is a loving father, who’s desperate to help his family back in Italy. In the very last scene, we start by seeing Catherine one again stealing his manliness away from him, by saying that â€Å"he’s a rat! He belongs in the sewers!† This shows us by now, not even his closest family can bear to be close to him after the unforgivable sin that he’s done. Only one character keeps faith in Eddie, and that’s Beatrice. She ironically stands by him all the time. But when Eddie sees Marco, he loses his mind, and starts to attack him verbally and physically. Eddie is blind to the fact that he is wrong, he isn’t enough of a man to face up to the fact, so he Marco to tell the people  that â€Å"what a liar you are!† This shows that Eddie is confident that he can have one successful blow at Marco, but he is wrong. Marco attacks back by calling Eddie an â€Å"†Animal! You go on your knees to me!†!† And he does this twice. This is one of the worst insults that a man in that time could call another. It shows us that Eddie is below the level of dignity shown by human beings, and is down there in the dumps, and by going on his knees shows that he’s at the same level as animals. Then both of them get ready to fight. Eddie at this point has nothing to lose, so he takes out a knife and at this point and â€Å"Louis (Eddie’s friend) halts and steps back from trying to stop the fight. This shows the power and status Eddie has just won by cheating. But in the end, Eddie had no chance of beating Marco, due to his strength. Marco managed to turn the blade around and stabbed Eddie. Eddie died, as a cheat, but he regains some dignity as he dies in Beatrice’s arms. This shows although all the horrid remarks and actions Eddie has made, Beatrice is there until the very end. Marco’s manliness during the play just grows and grows until this climax. Without a doubt, Miller has many different views on masculinity. You have Eddie and Marco, who are two extremely strong men, and you have Rodolfo, who is extremely keen on more feminine activates. In my opinion, there isn’t a lot of difference between these. All three are men in their different ways, but one thing in my view is a must is respect. And Eddie had lost it, all of it by the end. He used to be the king of the household before Marco and Rodolfo came along, and during that time, he was losing his respect due to the treatment he gave them, an example of this is when Eddie phoned the immigration office. In my opinion, Eddie knew he was losing respect from Beatrice because he demanded her â€Å"I want my respect, Beatrice.† This shows us that he worries what people think of him.

Social Capital has been described as involving egocentric, weak ties and socio-centric types of relationships

Social Capital has been described as involving egocentric, weak ties and socio-centric types of relationships. How might these concepts help to improve the way organisations generate new knowledge. Might some these concepts also act as a barrier to generating and sharing knowledge? Explain your answer. Introduction In contemporary, highly developing business environment, the success factors of many organisations have been affected with the rapid advancement in communication and ways of sharing knowledge. The knowledge economy has changed the basis of trading and doing business. Success and wealth of businesses no longer depends on their wealth of organisations but on the abilities and knowledge of their employees and the degree to which an organisation harnesses and develops those skills. The more effective the relationship between supplier and customer, the more successful an organisation is. This success depends on their abilities to operate in today's fast moving global marketplace. Defining the notion of Social Capital The notion of social capital first appeared in discussions of rural school community centers by Lyda Judson Hanifan's. Hanifan addressed the cultivation of good will, fellowship, sympathy and social intercourse among those that ‘made up a social unit.' More recently however, the work of Robert D. Putnam (1993, 2000) launched social capital as a focus for research and policy discussion. Putman defined the concept of social capital as â€Å"Features of social organisation, such as trust, norms and networks that can improve the efficiency of society by facilitating coordinated action† (Putnam, 1993). This definition of social capital can be criticised for adopting a single view, and being too narrow, as it ignores the fact that social capital can generate negative externalities as well as positive. Putman assumes ‘trust, norms and networks' to have positive outcomes for an individual, or a group however fails to recognise that it can be harmful for an organisation as a whole. Michael Woolcock on the other hand defined social capital as â€Å"the norms and social relations embedded in the social structure that enable people to coordinate action to achieve desired goals† This definition not only recognises both positive and negative externalities of social capital but focuses solely on sources of social capital, rather than also including the outcomes derived from it. One of the most famous examples where social capital is commonly referred to is in Silicon Valley (San Francisco). Silicon Valley is in the southern part of the San Francisco Bay Area in Northern California in the United States. It contains many high tech businesses that are supplying the global market with many innovating technologies and silicon microchips. In Silicon Valley, there is a very high level of knowledge held within individual firms, but this would be true whether they were located in clusters or in isolation. There is also a very high level of knowledge about the firms as well. This information is differentially more available to those in the Valley and in the network. This knowledge is not just technical, it is knowledge about who is a good manager or well connected. It is embedded in the social setting, a knowledge that comes from learning and being in the place where the knowledge is being used, and having an opportunity to use it in that setting. It is about knowled ge use and production in action. Social Capital – promotes knowledge sharing and communication Many contemporary theorists who conducted studies on social capital identified two differing perspectives within the concept. These concepts are socio-centric, and ego-centric. The socio-centric approach argues that the social structure of interpersonal contacts is important for organizational success (Sandefur and Laumann, 1998). A business can benefit from a strong social structure, by allowing employee cooperation that will enable flexibility and innovation. Knowledge sharing helps employees perform their jobs more effectively, retain their jobs, and guides them in personal and career development. It also rewards them for successful achievements, and brings more personal recognition so that knowledge sharing will become more practiced. By sharing and collaborating with others an employee is more likely to succeed in providing solutions to his/hers own jobs and by helping others achieve their objectives. The philosophy of modern knowledge management exponents is that ‘intrinsic motivation' is the only real motivator of knowledge sharing. This is where an individual, group or community are sympathetic to each other's goals, those of the organisation work for collective goals-if these are best achieved through sharing then this is what happens. Intrinsic motivation is making sure that individuals feel part of the business and culture through reward and recognition. The second perspective of social capital is concerned with the relationships between individuals. Sandefur and Laumann (1998) refer to this as the egocentric approach of social networks, where â€Å"an individual's social capital is characterized by their direct relationships with others and by the other people and relationships that they can reach through those to whom they are directly tied†. From this perspective, social capital is able to explain the differences in the success of individuals and firms in a competitive environment (Adler and Kwon, 1999). A learning organisation views its future and subsequent competitive advantage based on continuous learning and adaptive behaviour. It develops a culture and processes to improve its ability to learn and share both at an individual and organisational level. The main aim is to create a flexible, agile organisation able to handle uncertainty and then hopefully organisations will use this uncertainty to generate new ways of working, to build on this success and learn by mistakes. For example: a large multinational company, British Airways (BA) identified its culture as the biggest barrier it had to learning and sharing so it set out to create an environment where this was made easy. BA developed facilities for staff to access knowledge, libraries, have meeting rooms, training rooms that enhanced its culture. Therefore the facilitation of personal contacts and network, and the enhance role of training and development being a core was British Airways success for its new culture. Social capital – barrier to knowledge sharing and communication As developed in Ronald Burt's theory (1998), the socio-centric notion can act as a barrier to generating and sharing knowledge in an organisation. The socio-centric perspective includes the concept of power benefits acquired by individuals that control structural holes. This idea shows how certain individuals within an organisation may have power over groups of employees and act as the link between them. Such individuals are said to be ‘filling a structural hole', therefore their relative contacts have no direct contact with one another. This allows such an individual to have a certain level of manipulation over knowledge sharing between the two groups and benefit from the social capital derived from them. This can be both an advantage and disadvantage to the firm. It can be an advantage to the individual in that their social capital is increased, and that it allows the two departments to communicate ideas effectively. However the filling of structural holes with one individual could also act as a barrier to generating and sharing new knowledge within the firm. Because when implemented in a firm it means that individuals in different departments do not need to communicate between each other eliminating knowledge sharing within the individuals in each department. Within an egocentric network, sharing of knowledge can be one of the most difficult problems faced by knowledge organisations. In most organisations knowledge sharing requires a change in corporate culture, from ‘information is power' to ‘knowledge sharing build power.' Many organisations decide that the most effective way to encourage individual sharing appears to be through appraisal systems where individuals are asked to assess their own knowledge-sharing behaviours and consider their colleagues view of their sharing performance. The most obvious disadvantages are: an employee may fear senior experts or a supervisor. This fear can have an impact on the way the employee conveys his/her opinions. Another disadvantage is that employees can get compromising solutions from a group of experts with conflicting opinions. This would not give the knowledge engineer an accurate view of the knowledge needed. Also, there can also be a Lack of confidentiality as employees may feel threatened by knowing that their contributions will be shared with and evaluated or validated by other domain experts. However, the results of the appraisals may affect promotion and salary but their use is part of the development culture that includes knowledge sharing as a core competence. Conclusion Social capital has been described as a non-tradable form of capital that will depreciate if not used. Social capital increases in value through use, as relationships get stronger and weak ties are increased (Klaus Nielsen, 2003). In this article we have established that social capital is a rapidly growing notion, more commonly referred to by theorists when discussing issues of economics and organisational knowledge sharing. The concept has been criticised for the diversity of its definition, measurement challenges, and over-versatility (Woolcock and Schuller, 2000). These theorists concluded that social capital can have both a positive and a negative impact on the overall success of an organisation. Positive, in the aspects that a well working network of sharing knowledge can lead to innovation, and greater efficiency of the employees working relationships. Negative in the sense that strong social capital for an individual, or a group of employees does not necessarily guarantee a benefit on a macro scale for the organisation.

Bayesian Inference

Biostatistics (2010), 11, 3, pp. 397–412 doi:10. 1093/biostatistics/kxp053 Advance Access publication on December 4, 2009 Bayesian inference for generalized linear mixed models YOUYI FONG Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Department of Biostatistics, University of Washington, Seattle, WA 98112, USA ? HAVARD RUE Department of Mathematical Sciences, The Norwegian University for Science and Technology, N-7491 Trondheim, Norway JON WAKEFIELD? Departments of Statistics and Biostatistics, University of Washington, Seattle, WA 98112, USA [email  protected] ashington. edu S UMMARY Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample sizes especially, likelihood-based inference can be unreliable with variance components being particularly difficult to estimate. A Bayesian approach is appealing but has been hampered by the lack of a fast implementation, and the difficulty in specifying prior distributions with variance components again being particularly problematic.Here, we briefly review previous approaches to computation in Bayesian implementations of GLMMs and illustrate in detail, the use of integrated nested Laplace approximations in this context. We consider a number of examples, carefully specifying prior distributions on meaningful quantities in each case. The examples cover a wide range of data types including those requiring smoothing over time and a relatively complicated spline model for which we examine our prior specification in terms of the implied degrees of freedom.We conclude that Bayesian inference is now practically feasible for GLMMs and provides an attractive alternative to likelihood-based approaches such as penalized quasi-likelihood. As with likelihood-based approaches, great care is required in the analysis of clustered bina ry data since approximation strategies may be less accurate for such data. Keywords: Integrated nested Laplace approximations; Longitudinal data; Penalized quasi-likelihood; Prior specification; Spline models. 1.I NTRODUCTION Generalized linear mixed models (GLMMs) combine a generalized linear model with normal random effects on the linear predictor scale, to give a rich family of models that have been used in a wide variety of applications (see, e. g. Diggle and others, 2002; Verbeke and Molenberghs, 2000, 2005; McCulloch and others, 2008). This flexibility comes at a price, however, in terms of analytical tractability, which has a ? To whom correspondence should be addressed. c The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals. [email  protected] rg. 398 Y. F ONG AND OTHERS number of implications including computational complexity, and an unknown degree to which inference is dependent on modeling assumptions. Lik elihood-based inference may be carried out relatively easily within many software platforms (except perhaps for binary responses), but inference is dependent on asymptotic sampling distributions of estimators, with few guidelines available as to when such theory will produce accurate inference. A Bayesian approach is attractive, but requires the specification of prior distributions which is not straightforward, in particular for variance components.Computation is also an issue since the usual implementation is via Markov chain Monte Carlo (MCMC), which carries a large computational overhead. The seminal article of Breslow and Clayton (1993) helped to popularize GLMMs and placed an emphasis on likelihood-based inference via penalized quasi-likelihood (PQL). It is the aim of this article to describe, through a series of examples (including all of those considered in Breslow and Clayton, 1993), how Bayesian inference may be performed with computation via a fast implementation and with guidance on prior specification. The structure of this article is as follows.In Section 2, we define notation for the GLMM, and in Section 3, we describe the integrated nested Laplace approximation (INLA) that has recently been proposed as a computationally convenient alternative to MCMC. Section 4 gives a number of prescriptions for prior specification. Three examples are considered in Section 5 (with additional examples being reported in the supplementary material available at Biostatistics online, along with a simulation study that reports the performance of INLA in the binary response situation). We conclude the paper with a discussion in Section 6. 2.T HE G ENERALIZED LINEAR MIXED MODEL GLMMs extend the generalized linear model, as proposed by Nelder and Wedderburn (1972) and comprehensively described in McCullagh and Nelder (1989), by adding normally distributed random effects on the linear predictor scale. Suppose Yi j is of exponential family form: Yi j |? i j , ? 1 ? p(â₠¬ ¢), where p(†¢) is a member of the exponential family, that is, p(yi j |? i j , ? 1 ) = exp yi j ? i j ? b(? i j ) + c(yi j , ? 1 ) , a(? 1 ) Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 for i = 1, . . . , m units (clusters) and j = 1, . . , n i , measurements per unit and where ? i j is the (scalar) ? canonical parameter. Let ? i j = E[Yi j |? , b i , ? 1 ] = b (? i j ) with g(? i j ) = ? i j = x i j ? + z i j b i , where g(†¢) is a monotonic â€Å"link† function, x i j is 1 ? p, and z i j is 1 ? q, with ? a p ? 1 vector of fixed ? Q effects and b i a q ? 1 vector of random effects, hence ? i j = ? i j (? , b i ). Assume b i |Q ? N (0, Q ? 1 ), where ? the precision matrix Q = Q (? 2 ) depends on parameters ? 2 . For some choices of model, the matrix Q is singular; examples include random walk models (as considered in Section 5. ) and intrinsic conditional ? autoregressive models. We further assume tha t ? is assigned a normal prior distribution. Let ? = (? , b ) denote the G ? 1 vector of parameters assigned Gaussian priors. We also require priors for ? 1 (if not a constant) and for ? 2 . Let ? = (? 1 , ? 2 ) be the variance components for which non-Gaussian priors are ? assigned, with V = dim(? ). 3. I NTEGRATED NESTED L APLACE APPROXIMATION Before the MCMC revolution, there were few examples of the applications of Bayesian GLMMs since, outside of the linear mixed model, the models are analytically intractable.Kass and Steffey (1989) describe the use of Laplace approximations in Bayesian hierarchical models, while Skene and Wakefield Bayesian GLMMs 399 (1990) used numerical integration in the context of a binary GLMM. The use of MCMC for GLMMs is particularly appealing since the conditional independencies of the model may be exploited when the required conditional distributions are calculated. Zeger and Karim (1991) described approximate Gibbs sampling for GLMMs, with nonstandar d conditional distributions being approximated by normal distributions.More general Metropolis–Hastings algorithms are straightforward to construct (see, e. g. Clayton, 1996; Gamerman, 1997). The winBUGS (Spiegelhalter, Thomas, and Best, 1998) software example manuals contain many GLMM examples. There are now a variety of additional software platforms for fitting GLMMs via MCMC including JAGS (Plummer, 2009) and BayesX (Fahrmeir and others, 2004). A large practical impediment to data analysis using MCMC is the large computational burden. For this reason, we now briefly review the INLA computational approach upon which we concentrate.The method combines Laplace approximations and numerical integration in a very efficient manner (see Rue and others, 2009, for a more extensive treatment). For the GLMM described in Section 2, the posterior is given by m Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 ? y ? ? ? ?(? , ? |y ) ? ?(? |? )? (? ) i=1 y ? p(y i |? , ? ) m i=1 1 ? ? Q ? ? b ? ?(? )? (? )|Q (? 2 )|1/2 exp ? b T Q (? 2 )b + 2 y ? log p(y i |? , ? 1 ) , where y i = (yi1 , . . . , yin i ) is the vector of observations on unit/cluster i.We wish to obtain the posterior y y marginals ? (? g |y ), g = 1, . . . , G, and ? (? v |y ), v = 1, . . . , V . The number of variance components, V , should not be too large for accurate inference (since these components are integrated out via Cartesian product numerical integration, which does not scale well with dimension). We write y ? (? g |y ) = which may be evaluated via the approximation y ? (? g |y ) = K ? ? y ? ?(? g |? , y ) ? ?(? |y )d? , ? ? y ? ?(? g |? , y ) ? ? (? |y )d? ? y ? ? (? g |? k , y ) ? ? (? k |y ) ? k, ? (3. 1) k=1 here Laplace (or other related analytical approximations) are applied to carry out the integrations required ? ? for evaluation of ? (? g |? , y ). To produce the grid of points {? k , k = 1, . . . , K } over which numerical inte? y gration is performed, the mode of ? (? |y ) is located, and the Hessian is approximated, from which the grid is created and exploited in (3. 1). The output of INLA consists of posterior marginal distributions, which can be summarized via means, variances, and quantiles. Importantly for model comparison, the normaly izing constant p(y ) is calculated.The evaluation of this quantity is not straightforward using MCMC (DiCiccio and others, 1997; Meng and Wong, 1996). The deviance information criterion (Spiegelhalter, Best, and others, 1998) is popular as a model selection tool, but in random-effects models, the implicit approximation in its use is valid only when the effective number of parameters is much smaller than the number of independent observations (see Plummer, 2008). 400 Y. F ONG AND OTHERS 4. P RIOR DISTRIBUTIONS 4. 1 Fixed effects Recall that we assume ? is normally distributed. Often there will be sufficient information in the data for ? o be well estimated with a n ormal prior with a large variance (of course there will be circumstances under which we would like to specify more informative priors, e. g. when there are many correlated covariates). The use of an improper prior for ? will often lead to a proper posterior though care should be taken. For example, Wakefield (2007) shows that a Poisson likelihood with a linear link can lead to an improper posterior if an improper prior is used. Hobert and Casella (1996) discuss the use of improper priors in linear mixed effects models.If we wish to use informative priors, we may specify independent normal priors with the parameters for each component being obtained via specification of 2 quantiles with associated probabilities. For logistic and log-linear models, these quantiles may be given on the exponentiated scale since these are more interpretable (as the odds ratio and rate ratio, respectively). If ? 1 and ? 2 are the quantiles on the exponentiated scale and p1 and p2 are the associated probab ilities, then the parameters of the normal prior are given by ? = ? = z 2 log(? 1 ) ? z 1 log(? 2 ) , z2 ? 1 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 log(? 2 ) ? log(? 1 ) , z2 ? z1 where z 1 and z 2 are the p1 and p2 quantiles of a standard normal random variable. For example, in an epidemiological context, we may wish to specify a prior on a relative risk parameter, exp(? 1 ), which has a median of 1 and a 95% point of 3 (if we think it is unlikely that the relative risk associated with a unit increase in exposure exceeds 3). These specifications lead to ? 1 ? N (0, 0. 6682 ). 4. 2 Variance componentsWe begin by describing an approach for choosing a prior for a single random effect, based on Wakefield (2009). The basic idea is to specify a range for the more interpretable marginal distribution of bi and use this to drive specification of prior parameters. We state a trivial lemma upon which prior specification is ba sed, but first define some notation. We write ? ? Ga(a1 , a2 ) for the gamma distribution with un? normalized density ? a1 ? 1 exp(? a2 ? ). For q-dimensional x , we write x ? Tq (? , , d) for the Student’s x x t distribution with unnormalized density [1 + (x ? ? )T ? 1 (x ? )/d]? (d+q)/2 . This distribution has location ? , scale matrix , and degrees of freedom d. L EMMA 1 Let b|? ? N (0, ? ?1 ) and ? ? Ga(a1 , a2 ). Integration over ? gives the marginal distribution of b as T1 (0, a2 /a1 , 2a1 ). To decide upon a prior, we give a range for a generic random effect b and specify the degrees of freev d dom, d, and then solve for a1 and a2 . For the range (? R, R), we use the relationship  ±t1? (1? q)/2 a2 /a1 = d  ±R, where tq is the 100 ? qth quantile of a Student t random variable with d degrees of freedom, to give d a1 = d/2 and a2 = R 2 d/2(t1? (1? q)/2 )2 .In the linear mixed effects model, b is directly interpretable, while for binomial or Poisson models, it is more appropriate to think in terms of the marginal distribution of exp(b), the residual odds and rate ratio, respectively, and this distribution is log Student’s t. For example, if we choose d = 1 (to give a Cauchy marginal) and a 95% range of [0. 1, 10], we take R = log 10 and obtain a = 0. 5 and b = 0. 0164. Bayesian GLMMs 401 ?1 Another convenient choice is d = 2 to give the exponential distribution with mean a2 for ? ?2 . This leads to closed-form expressions for the more interpretable quantiles of ? o that, for example, if we 2 specify the median for ? as ? m , we obtain a2 = ? m log 2. Unfortunately, the use of Ga( , ) priors has become popular as a prior for ? ?2 in a GLMM context, arising from their use in the winBUGS examples manual. As has been pointed out many times (e. g. Kelsall and Wakefield, 1999; Gelman, 2006; Crainiceanu and others, 2008), this choice places the majority of the prior mass away from zero and leads to a marginal prior for the random effects which is Student’s t with 2 degrees of freedom (so that the tails are much heavier than even a Cauchy) and difficult to justify in any practical setting.We now specify another trivial lemma, but first establish notation for the Wishart distribution. For the q ? q nonsingular matrix z , we write z ? Wishartq (r, S ) for the Wishart distribution with unnormalized Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Q Lemma: Let b = (b1 , . . . , bq ), with b |Q ? iid Nq (0, Q ? 1 ), Q ? Wishartq (r, S ). Integration over Q b as Tq (0, [(r ? q + 1)S ]? 1 , r ? q + 1). S gives the marginal distribution of The margins of a multivariate Student’s t are t also, which allows r and S to be chosen as in the univariate case.Specifically, the kth element of a generic random effect, bk , follows a univariate Student t distribution with location 0, scale S kk /(r ? q + 1), and degrees of freedom d = r ? q + 1, where S kk d is element (k, k) of the inverse of S . We obtain r = d + q ? 1 and S kk = (t1? (1? q)/2 )2 /(d R 2 ). If a priori b are correlated we may specify S jk = 0 for j = k and we have no reason to believe that elements of S kk = 1/Skk , to recover the univariate specification, recognizing that with q = 1, the univariate Wishart has parameters a1 = r/2 and a2 = 1/(2S).If we believe that elements of b are dependent then we may specify the correlations and solve for the off-diagonal elements of S . To ensure propriety of the posterior, proper priors are required for ; Zeger and Karim (1991) use an improper prior for , so that the posterior is improper also. 4. 3 Effective degrees of freedom variance components prior z z z z density |z |(r ? q? 1)/2 exp ? 1 tr(z S ? 1 ) . This distribution has E[z ] = r S and E[z ? 1 ] = S ? 1 /(r ? q ? 1), 2 and we require r > q ? 1 for a proper distribution.In Section 5. 3, we describe the GLMM representation of a spline model. A generic linear spline model is given by K yi = x i ? + k=1 z ik bk + i , where x i is a p ? 1 vector of covariates with p ? 1 associated fixed effects ? , z ik denote the spline 2 basis, bk ? iid N (0, ? b ), and i ? iid N (0, ? 2 ), with bk and i independent. Specification of a prior for 2 is not straightforward, but may be of great importance since it contributes to determining the amount ? b of smoothing that is applied. Ruppert and others (2003, p. 77) raise concerns, â€Å"about the instability of automatic smoothing parameter selection even for single predictor models†, and continue, â€Å"Although we are attracted by the automatic nature of the mixed model-REML approach to fitting additive models, we discourage blind acceptance of whatever answer it provides and recommend looking at other amounts of smoothing†. While we would echo this general advice, we believe that a Bayesian mixed model approach, with carefully chosen priors, can increase the stability of the mixed model representation. There has be en 2 some discussion of choice of prior for ? in a spline context (Crainiceanu and others, 2005, 2008). More general discussion can be found in Natarajan and Kass (2000) and Gelman (2006). In practice (e. g. Hastie and Tibshirani, 1990), smoothers are often applied with a fixed degrees of freedom. We extend this rationale by examining the prior degrees of freedom that is implied by the choice 402 Y. F ONG AND OTHERS ?2 ? b ? Ga(a1 , a2 ). For the general linear mixed model y = x ? + zb + , we have x z where C = [x |z ] is n ? ( p + K ) and C y = x ? + z b = C (C T C + 0 p? p 0K ? p )? 1 C T y , = 0 p? K 2 cov(b )? 1 b ? )? 1 C T C }, Downloaded from http://biostatistics. xfordjournals. org/ at Cornell University Library on April 20, 2013 (see, e. g. Ruppert and others, 2003, Section 8. 3). The total degrees of freedom associated with the model is C df = tr{(C T C + which may be decomposed into the degrees of freedom associated with ? and b , and extends easily to situations in which we have additional random effects, beyond those associated with the spline basis (such an example is considered in Section 5. 3). In each of these situations, the degrees of freedom associated C with the respective parameter is obtained by summing the appropriate diagonal elements of (C T C + )? C T C . Specifically, if we have j = 1, . . . , d sets of random-effect parameters (there are d = 2 in the model considered in Section 5. 3) then let E j be the ( p + K ) ? ( p + K ) diagonal matrix with ones in the diagonal positions corresponding to set j. Then the degrees of freedom associated with this set is E C df j = tr{E j (C T C + )? 1 C T C . Note that the effective degrees of freedom changes as a function of K , as expected. To evaluate , ? 2 is required. If we specify a proper prior for ? 2 , then we may specify the 2 2 joint prior as ? (? b , ? 2 ) = ? (? 2 )? (? b |? 2 ).Often, however, we assume the improper prior ? (? 2 ) ? 1/? 2 since the data provide sufficient information with respect to ? 2 . Hence, we have found the substitution of an estimate for ? 2 (for example, from the fitting of a spline model in a likelihood implementation) to be a practically reasonable strategy. As a simple nonspline demonstration of the derived effective degrees of freedom, consider a 1-way analysis of variance model Yi j = ? 0 + bi + i j 2 with bi ? iid N (0, ? b ), i j ? iid N (0, ? 2 ) for i = 1, . . . , m = 10 groups and j = 1, . . . , n = 5 observa? 2 tions per group. For illustration, we assume ? ? Ga(0. 5, 0. 005). Figure 1 displays the prior distribution for ? , the implied prior distribution on the effective degrees of freedom, and the bivariate plot of these quantities. For clarity of plotting, we exclude a small number of points beyond ? > 2. 5 (4% of points). In panel (c), we have placed dashed horizontal lines at effective degrees of freedom equal to 1 (complete smoothing) and 10 (no smoothing). From panel (b), we conclude that here the prior choice favors q uite strong smoothing. This may be contrasted with the gamma prior with parameters (0. 001, 0. 001), which, in this example, gives reater than 99% of the prior mass on an effective degrees of freedom greater than 9. 9, again showing the inappropriateness of this prior. It is appealing to extend the above argument to nonlinear models but unfortunately this is not straightforward. For a nonlinear model, the degrees of freedom may be approximated by C df = tr{(C T W C + where W = diag Vi? 1 d? i dh 2 )? 1 C T W C }, and h = g ? 1 denotes the inverse link function. Unfortunately, this quantity depends on ? and b , which means that in practice, we would have to use prior estimates for all of the parameters, which may not be practically possible.Fitting the model using likelihood and then substituting in estimates for ? and b seems philosophically dubious. Bayesian GLMMs 403 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 1. Gamma prior for ? ?2 with parameters 0. 5 and 0. 005, (a) implied prior for ? , (b) implied prior for the effective degrees of freedom, and (c) effective degrees of freedom versus ? . 4. 4 Random walk models Conditionally represented smoothing models are popular for random effects in both temporal and spatial applications (see, e. g. Besag and others, 1995; Rue and Held, 2005).For illustration, consider models of the form ? (m? r ) Q u 2 exp ? p(u |? u ) = (2? )? (m? r )/2 |Q |1/2 ? u 1 T u Qu , 2 2? u (4. 1) 404 Y. F ONG AND OTHERS where u = (u 1 , . . . , u m ) is the collection of random effects, Q is a (scaled) â€Å"precision† matrix of rank Q m ? r , whose form is determined by the application at hand, and |Q | is a generalized determinant which is the product over the m ? r nonzero eigenvalues of Q . Picking a prior for ? u is not straightforward because ? u has an interpretation as the conditional standard deviation, where the elements that are conditioned upon depend s on the application.We may simulate realizations from (4. 1) to examine candidate prior distributions. Due to the rank deficiency, (4. 1) does not define a probability density, and so we cannot directly simulate from this prior. However, Rue and Held (2005) give an algorithm for generating samples from (4. 1): 1. Simulate z j ? N (0, 1 ), for j = m ? r + 1, . . . , m, where ? j are the eigenvalues of Q (there are j m ? r nonzero eigenvalues as Q has rank m ? r ). 2. Return u = z m? r +1 e n? r +1 + z 3 e 3 + †¢ †¢ †¢ + z n e m = E z , where e j are the corresponding eigenvectors of Q , E is the m ? (m ? ) matrix with these eigenvectors as columns, and z is the (m ? r ) ? 1 vector containing z j , j = m ? r + 1, . . . , m. The simulation algorithm is conditioned so that samples are zero in the null-space of Q ; if u is a sample and the null-space is spanned by v 1 and v 2 , then u T v 1 = u T v 2 = 0. For example, suppose Q 1 = 0 so that the null-space is spanned by 1, and the rank deficiency is 1. Then Q is improper since the eigenvalue corresponding to 1 is zero, and samples u produced by the algorithm are such that u T 1 = 0. In Section 5. 2, we use this algorithm to evaluate different priors via simulation.It is also useful to note that if we wish to compute the marginal variances only, simulation is not required, as they are available as the diagonal elements of the matrix j 1 e j e T . j j 5. E XAMPLES Here, we report 3 examples, with 4 others described in the supplementary material available at Biostatistics online. Together these cover all the examples in Breslow and Clayton (1993), along with an additional spline example. In the first example, results using the INLA numerical/analytical approximation described in Section 3 were compared with MCMC as implemented in the JAGS software (Plummer, 2009) and found to be accurate.For the models considered in the second and third examples, the approximation was compared with the MCMC implement ation contained in the INLA software. 5. 1 Longitudinal data We consider the much analyzed epilepsy data set of Thall and Vail (1990). These data concern the number ? of seizures, Yi j for patient i on visit j, with Yi j |? , b i ? ind Poisson(? i j ), i = 1, . . . , 59, j = 1, . . . , 4. We concentrate on the 3 random-effects models fitted by Breslow and Clayton (1993): log ? i j = x i j ? + b1i , (5. 1) (5. 2) (5. 3) Downloaded from http://biostatistics. oxfordjournals. rg/ at Cornell University Library on April 20, 2013 log ? i j = x i j ? + b1i + b2i V j /10, log ? i j = x i j ? + b1i + b0i j , where x i j is a 1 ? 6 vector containing a 1 (representing the intercept), an indicator for baseline measurement, a treatment indicator, the baseline by treatment interaction, which is the parameter of interest, age, and either an indicator of the fourth visit (models (5. 1) and (5. 2) and denoted V4 ) or visit number coded ? 3, ? 1, +1, +3 (model (5. 3) and denoted V j /10) and ? is the associated fixed effect. All 3 models 2 include patient-specific random effects b1i ? N 0, ? , while in model (5. 2), we introduce independent 2 ). Model (5. 3) includes random effects on the slope associated with â€Å"measurement errors,† b0i j ? N (0, ? 0 Bayesian GLMMs 405 Table 1. PQL and INLA summaries for the epilepsy data Variable Base Trt Base ? Trt Age V4 or V/10 ? 0 ? 1 ? 2 Model (5. 1) PQL 0. 87  ± 0. 14 ? 0. 91  ± 0. 41 0. 33  ± 0. 21 0. 47  ± 0. 36 ? 0. 16  ± 0. 05 — 0. 53  ± 0. 06 — INLA 0. 88  ± 0. 15 ? 0. 94  ± 0. 44 0. 34  ± 0. 22 0. 47  ± 0. 38 ? 0. 16  ± 0. 05 — 0. 56  ± 0. 08 — Model (5. 2) PQL 0. 86  ± 0. 13 ? 0. 93  ± 0. 40 0. 34  ± 0. 21 0. 47  ± 0. 35 ? 0. 10  ± 0. 09 0. 36  ± 0. 04 0. 48  ± 0. 06 — INLA 0. 8  ± 0. 15 ? 0. 96  ± 0. 44 0. 35  ± 0. 23 0. 48  ± 0. 39 ? 0. 10  ± 0. 09 0. 41  ± 0. 04 0. 53  ± 0. 07 — Model (5. 3) PQL 0. 87  ± 0. 14 ? 0. 91  ± 0. 41 0. 33  ± 0. 21 0. 46  ± 0. 36 ? 0. 26  ± 0. 16 — 0. 52  ± 0. 06 0. 74  ± 0. 16 INLA 0. 88  ± 0. 14 ? 0. 94  ± 0. 44 0. 34  ± 0. 22 0. 47  ± 0. 38 ? 0. 27  ± 0. 16 — 0. 56  ± 0. 06 0. 70  ± 0. 14 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 visit, b2i with b1i b2i ? N (0, Q ? 1 ). (5. 4) We assume Q ? Wishart(r, S ) with S = S11 S12 . For prior specification, we begin with the bivariate S21 S22 model and assume that S is diagonal.We assume the upper 95% point of the priors for exp(b1i ) and exp(b2i ) are 5 and 4, respectively, and that the marginal distributions are t with 4 degrees of freedom. Following the procedure outlined in Section 4. 2, we obtain r = 5 and S = diag(0. 439, 0. 591). We take ? 2 the prior for ? 1 in model (5. 1) to be Ga(a1 , a2 ) with a1 = (r ? 1)/2 = 2 and a2 = 1/2S11 = 1. 140 (so that this prior coincides with the marginal prior obtained from the bivariat e specification). In model (5. 2), ? 2 ? 2 we assume b1i and b0i j are independent, and that ? 0 follows the same prior as ? , that is, Ga(2, 1. 140). We assume a flat prior on the intercept, and assume that the rate ratios, exp(? j ), j = 1, . . . , 5, lie between 0. 1 and 10 with probability 0. 95 which gives, using the approach described in Section 4. 1, a normal prior with mean 0 and variance 1. 172 . Table 1 gives PQL and INLA summaries for models (5. 1–5. 3). There are some differences between the PQL and Bayesian analyses, with slightly larger standard deviations under the latter, which probably reflects that with m = 59 clusters, a little accuracy is lost when using asymptotic inference.There are some differences in the point estimates which is at least partly due to the nonflat priors used—the priors have relatively large variances, but here the data are not so abundant so there is sensitivity to the prior. Reassuringly under all 3 models inference for the bas eline-treatment interaction of interest is virtually y identical and suggests no significant treatment effect. We may compare models using log p(y ): for 3 models, we obtain values of ? 674. 8, ? 638. 9, and ? 665. 5, so that the second model is strongly preferred. 5. Smoothing of birth cohort effects in an age-cohort model We analyze data from Breslow and Day (1975) on breast cancer rates in Iceland. Let Y jk be the number of breast cancer of cases in age group j (20–24,. . . , 80–84) and birth cohort k (1840–1849,. . . ,1940–1949) with j = 1, . . . , J = 13 and k = 1, . . . , K = 11. Following Breslow and Clayton (1993), we assume Y jk |? jk ? ind Poisson(? jk ) with log ? jk = log n jk + ? j + ? k + vk + u k (5. 5) and where n jk is the person-years denominator, exp(? j ), j = 1, . . . , J , represent fixed effects for age relative risks, exp(? is the relative risk associated with a one group increase in cohort group, vk ? iid 406 Y. F ONG AND OTHERS 2 N (0, ? v ) represent unstructured random effects associated with cohort k, with smooth cohort terms u k following a second-order random-effects model with E[u k |{u i : i < k}] = 2u k? 1 ? u k? 2 and Var(u k |{u i : 2 i < k}) = ? u . This latter model is to allow the rates to vary smoothly with cohort. An equivalent representation of this model is, for 2 < k < K ? 1, 1 E[u k |{u l : l = k}] = (4u k? 1 + 4u k+1 ? u k? 2 ? u k+2 ), 6 Var(u k |{u l : l = k}) = 2 ? . 6 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 The rank of Q in the (4. 1) representation of this model is K ? 2 reflecting that both the overall level and the overall trend are aliased (hence the appearance of ? in (5. 5)). The term exp(vk ) reflects the unstructured residual relative risk and, following the argument in Section 4. 2, we specify that this quantity should lie in [0. 5, 2. 0] with probability 0. 95, with a marginal log Cauchy ? 2 distribution, to obtain the gamma prior ? v ? Ga(0. 5, 0. 00149).The term exp(u k ) reflects the smooth component of the residual relative risk, and the specification of a 2 prior for the associated variance component ? u is more difficult, given its conditional interpretation. Using the algorithm described in Section 4. 2, we examined simulations of u for different choices of gamma ? 2 hyperparameters and decided on the choice ? u ? Ga(0. 5, 0. 001); Figure 2 shows 10 realizations from the prior. The rationale here is to examine realizations to see if they conform to our prior expectations and in particular exhibit the required amount of smoothing.All but one of the realizations vary smoothly across the 11 cohorts, as is desirable. Due to the tail of the gamma distribution, we will always have some extreme realizations. The INLA results, summarized in graphical form, are presented in Figure 2(b), alongside likelihood fits in which the birth cohort effect is incorporated as a linear term and as a f actor. We see that the smoothing model provides a smooth fit in birth cohort, as we would hope. 5. 3 B-Spline nonparametric regression We demonstrate the use of INLA for nonparametric smoothing using O’Sullivan splines, which are based on a B-spline basis.We illustrate using data from Bachrach and others (1999) that concerns longitudinal measurements of spinal bone mineral density (SBMD) on 230 female subjects aged between 8 and 27, and of 1 of 4 ethnic groups: Asian, Black, Hispanic, and White. Let yi j denote the SBMD measure for subject i at occasion j, for i = 1, . . . , 230 and j = 1, . . . , n i with n i being between 1 and 4. Figure 3 shows these data, with the gray lines indicating measurements on the same woman. We assume the model K Yi j = x i ? 1 + agei j ? 2 + k=1 z i jk b1k + b2i + ij, where x i is a 1 ? vector containing an indicator for the ethnicity of individual i, with ? 1 the associated 4 ? 1 vector of fixed effects, z i jk is the kth basis associated with age, with associated parameter b1k ? 2 2 N (0, ? 1 ), and b2i ? N (0, ? 2 ) are woman-specific random effects, finally, i j ? iid N (0, ? 2 ). All random terms are assumed independent. Note that the spline model is assumed common to all ethnic groups and all women, though it would be straightforward to allow a different spline for each ethnicity. Writing this model in the form y = x ? + z 1b1 + z 2b 2 + = C ? + . Bayesian GLMMs 407Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 2. (a) Ten realizations (on the relative risk scale) from the random effects second-order random walk model in which the prior on the random-effects precision is Ga(0. 5,0. 001), (b) summaries of fitted models: the solid line corresponds to a log-linear model in birth cohort, the circles to birth cohort as a factor, and â€Å"+† to the Bayesian smoothing model. we use the method described in Section 4. 3 to examine the effective number of parameters implied by the ? 2 ? 2 priors ? 1 ? Ga(a1 , a2 ) and ? 2 ? Ga(a3 , a4 ).To fit the model, we first use the R code provided in Wand and Ormerod (2008) to construct the basis functions, which are then input to the INLA program. Running the REML version of the model, we obtain 2 ? = 0. 033 which we use to evaluate the effective degrees of freedoms associated with priors for ? 1 and 2 . We assume the usual improper prior, ? (? 2 ) ? 1/? 2 for ? 2 . After some experimentation, we settled ? 2 408 Y. F ONG AND OTHERS Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 3. SBMD versus age by ethnicity. Measurements on the same woman are joined with gray lines.The solid curve corresponds to the fitted spline and the dashed lines to the individual fits. ?2 2 on the prior ? 1 ? Ga(0. 5, 5 ? 10? 6 ). For ? 2 , we wished to have a 90% interval for b2i of  ±0. 3 which, ? 2 with 1 degree of freedom for the marginal distributio n, leads to ? 2 ? Ga(0. 5, 0. 00113). Figure 4 shows the priors for ? 1 and ? 2 , along with the implied effective degrees of freedom under the assumed priors. For the spline component, the 90% prior interval for the effective degrees of freedom is [2. 4,10]. Table 2 compares estimates from REML and INLA implementations of the model, and we see close correspondence between the 2.Figure 4 also shows the posterior medians for ? 1 and ? 2 and for the 2 effective degrees of freedom. For the spline and random effects these correspond to 8 and 214, respectively. The latter figure shows that there is considerable variability between the 230 women here. This is confirmed in Figure 3 where we observe large vertical differences between the profiles. This figure also shows the fitted spline, which appears to mimic the trend in the data well. 5. 4 Timings For the 3 models in the longitudinal data example, INLA takes 1 to 2 s to run, using a single CPU.To get estimates with similar precision wit h MCMC, we ran JAGS for 100 000 iterations, which took 4 to 6 min. For the model in the temporal smoothing example, INLA takes 45 s to run, using 1 CPU. Part of the INLA procedure can be executed in a parallel manner. If there are 2 CPUs available, as is the case with today’s prevalent INTEL Core 2 Duo processors, INLA only takes 27 s to run. It is not currently possible to implement this model in JAGS. We ran the MCMC utility built into the INLA software for 3. 6 million iterations, to obtain estimates of comparable accuracy, which took 15 h.For the model in the B-spline nonparametric regression example, INLA took 5 s to run, using a single CPU. We ran the MCMC utility built into the INLA software for 2. 5 million iterations to obtain estimates of comparable accuracy, the analysis taking 40 h. Bayesian GLMMs 409 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 4. Prior summaries: (a) ? 1 , the standard deviation of the spline coefficients, (b) effective degrees of freedom associated with the prior for the spline coefficients, (c) effective degrees of freedom versus ? , (d) ? 2 , the standard deviation of the between-individual random effects, (e) effective degrees of freedom associated with the individual random effects, and (f) effective degrees of freedom versus ? 2 . The vertical dashed lines on panels (a), (b), (d), and (e) correspond to the posterior medians. Table 2. REML and INLA summaries for spinal bone data. Intercept corresponds to Asian group Variable Intercept Black Hispanic White Age ? 1 ? 2 ? REML 0. 560  ± 0. 029 0. 106  ± 0. 021 0. 013  ± 0. 022 0. 026  ± 0. 022 0. 021  ± 0. 002 0. 018 0. 109 0. 033 INLA 0. 563  ± 0. 031 0. 106  ± 0. 021 0. 13  ± 0. 022 0. 026  ± 0. 022 0. 021  ± 0. 002 0. 024  ± 0. 006 0. 109  ± 0. 006 0. 033  ± 0. 002 Note: For the entries marked with a standard errors were unavailable. 410 Y. F ONG AND OTHERS 6. D ISCUSSION In t his paper, we have demonstrated the use of the INLA computational method for GLMMs. We have found that the approximation strategy employed by INLA is accurate in general, but less accurate for binomial data with small denominators. The supplementary material available at Biostatistics online contains an extensive simulation study, replicating that presented in Breslow and Clayton (1993).There are some suggestions in the discussion of Rue and others (2009) on how to construct an improved Gaussian approximation that does not use the mode and the curvature at the mode. It is likely that these suggestions will improve the results for binomial data with small denominators. There is an urgent need for diagnosis tools to flag when INLA is inaccurate. Conceptually, computation for nonlinear mixed effects models (Davidian and Giltinan, 1995; Pinheiro and Bates, 2000) can also be handled by INLA but this capability is not currently available. The website www. r-inla. rg contains all the data and R scripts to perform the analyses and simulations reported in the paper. The latest release of software to implement INLA can also be found at this site. Recently, Breslow (2005) revisited PQL and concluded that, â€Å"PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. † We believe that INLA provides an attractive alternative to PQL for GLMMs, and we hope that this paper stimulates the greater use of Bayesian methods for this class. Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013S UPPLEMENTARY MATERIAL Supplementary material is available at http://biostatistics. oxfordjournals. org. ACKNOWLEDGMENT Conflict of Interest: None declared. F UNDING National Institutes of Health (R01 CA095994) to J. W. Statistics for Innovation (sfi. nr. no) to H. R. R EFERENCES BACHRACH , L. K. , H ASTIE , T. , WANG , M. C. , NARASIMHAN , B. AND M ARCUS , R. (1999). Bone mineral acquisition in healthy Asian, Hispanic, Black and Caucasian youth. A longitudinal study. The Journal of Clinical Endocrinology and Metabolism 84, 4702–4712. B ESAG , J. , G REEN , P. J. , H IGDON , D. AND M ENGERSEN , K. 1995). Bayesian computation and stochastic systems (with discussion). Statistical Science 10, 3–66. B RESLOW, N. E. (2005). Whither PQL? In: Lin, D. and Heagerty, P. J. (editors), Proceedings of the Second Seattle Symposium. New York: Springer, pp. 1–22. B RESLOW, N. E. AND C LAYTON , D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25. B RESLOW, N. E. AND DAY, N. E. (1975). Indirect standardization and multiplicative models for rates, with reference to the age adjustment of cancer incidence and relative frequency data.Journal of Chronic Diseases 28, 289–301. C LAYTON , D. G. (1996). Generalized linear mixed models. In: Gilks, W. R. , Richardson, S. and S piegelhalter, D. J. (editors), Markov Chain Monte Carlo in Practice. London: Chapman and Hall, pp. 275–301. Bayesian GLMMs 411 C RAINICEANU , C. M. , D IGGLE , P. J. AND ROWLINGSON , B. (2008). Bayesian analysis for penalized spline regression using winBUGS. Journal of the American Statistical Association 102, 21–37. C RAINICEANU , C. M. , RUPPERT, D. AND WAND , M. P. (2005). Bayesian analysis for penalized spline regression using winBUGS. Journal of Statistical Software 14.DAVIDIAN , M. AND G ILTINAN , D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman and Hall. D I C ICCIO , T. J. , K ASS , R. E. , R AFTERY, A. AND WASSERMAN , L. (1997). Computing Bayes factors by combining simulation and asymptotic approximations. Journal of the American Statistical Association 92, 903–915. Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 D IGGLE , P. , H EAGERTY, P. , L IANG , K. -Y. Oxford: Oxford University Press. AND Z EGER , S. (2002). Analysis of Longitudinal Data, 2nd edition. FAHRMEIR , L. , K NEIB , T.AND L ANG , S. (2004). Penalized structured additive regression for space-time data: a Bayesian perspective. Statistica Sinica 14, 715–745. G AMERMAN , D. (1997). Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing 7, 57–68. G ELMAN , A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515–534. H ASTIE , T. J. AND T IBSHIRANI , R. J. (1990). Generalized Additive Models. London: Chapman and Hall. H OBERT, J. P. AND C ASELLA , G. (1996). The effect of improper priors on Gibbs sampling in hierarchical linear mixed models.Journal of the American Statistical Association 91, 1461–1473. K ASS , R. E. AND S TEFFEY, D. (1989). Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). Journal of the American Statistical Association 84, 717–726. K ELSALL , J. E. AND WAKEFIELD , J. C. (1999). Discussion of â€Å"Bayesian models for spatially correlated disease and exposure data† by N. Best, I. Waller, A. Thomas, E. Conlon and R. Arnold. In: Bernardo, J. M. , Berger, J. O. , Dawid, A. P. and Smith, A. F. M. (editors), Sixth Valencia International Meeting on Bayesian Statistics. London: Oxford University Press.M C C ULLAGH , P. AND N ELDER , J. A. (1989). Generalized Linear Models, 2nd edition. London: Chapman and Hall. M C C ULLOCH , C. E. , S EARLE , S. R. AND N EUHAUS , J. M. (2008). Generalized, Linear, and Mixed Models, 2nd edition. New York: John Wiley and Sons. M ENG , X. AND W ONG , W. (1996). Simulating ratios of normalizing constants via a simple identity. Statistical Sinica 6, 831–860. NATARAJAN , R. AND K ASS , R. E. (2000). Reference Bayesian methods for generalized linear mixed models. Journal of the American Statistical Association 95, 22 7–237. N ELDER , J. AND W EDDERBURN , R. (1972). Generalized linear models.Journal of the Royal Statistical Society, Series A 135, 370–384. P INHEIRO , J. C. AND BATES , D. M. (2000). Mixed-Effects Models in S and S-plus. New York: Springer. P LUMMER , M. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics 9, 523–539. P LUMMER , M. (2009). Jags version 1. 0. 3 manual. Technical Report. RUE , H. AND H ELD , L. (2005). Gaussian Markov Random Fields: Thoery and Application. Boca Raton: Chapman and Hall/CRC. RUE , H. , M ARTINO , S. AND C HOPIN , N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested laplace approximations (with discussion).Journal of the Royal Statistical Society, Series B 71, 319–392. 412 RUPPERT, D. R. , WAND , M. P. University Press. AND Y. F ONG AND OTHERS C ARROLL , R. J. (2003). Semiparametric Regression. New York: Cambridge S KENE , A. M. AND WAKEFIELD , J. C. (1990). Hie rarchical models for multi-centre binary response studies. Statistics in Medicine 9, 919–929. S PIEGELHALTER , D. , B EST, N. , C ARLIN , B. AND VAN DER L INDE , A. (1998). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583–639. S PIEGELHALTER , D. J. , T HOMAS , A.AND B EST, N. G. (1998). WinBUGS User Manual. Version 1. 1. 1. Cambridge. T HALL , P. F. AND VAIL , S. C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671. V ERBEKE , G. V ERBEKE , G. AND AND Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 M OLENBERGHS , G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer. M OLENBERGHS , G. (2005). Models for Discrete Longitudinal Data. New York: Springer. WAKEFIELD , J. C. (2007). Disease mapping and spatial regression with count data.Biostatistics 8, 158–1 83. WAKEFIELD , J. C. (2009). Multi-level modelling, the ecologic fallacy, and hybrid study designs. International Journal of Epidemiology 38, 330–336. WAND , M. P. AND O RMEROD , J. T. (2008). On semiparametric regression with O’Sullivan penalised splines. Australian and New Zealand Journal of Statistics 50, 179–198. Z EGER , S. L. AND K ARIM , M. R. (1991). Generalized linear models with random effects: a Gibbs sampling approach. Journal of the American Statistical Association 86, 79–86. [Received September 4, 2009; revised November 4, 2009; accepted for publication November 6, 2009]

Common law Essay Example | Topics and Well Written Essays - 2000 words - 1

Common law - Essay Example Wirral Utilities has dug a trench along the pavement and so as to prevent anybody falling in the trench, placed at one end a long handed hammer and at the other some picks and shovels. One end of the hammer lay on the pavement, and the other was hooked on to some railings. These precautions would have been sufficient for the public at large who used the pavement, but the claimant was special, in that he was blind. While walking to his work along the pavement as usual, he had a fall because he tripped over the end of the hammer lying on the pavement. Normal people would have seen the danger, but the claimant did not. He was himself not negligent. His white stick had passed over the hammer. As a result of the fall, he banged his head against the pavement. In the resultant injury, he became deaf and had to retire early from the job. The claimant has sued Wirral Utilities for the tort of negligence. The case is founded on four scenarios or likely turns of event. First is whether Wirral Utilities owed any duty of care to the public at large who utilized the pavement. The next question is whether Wirral utilities breached that duty owed to the claimant. It is the third point to be considered whether the damage to the claimant i.e. the injury caused to him arising from such negligence was foreseeable by a party in a position as Wirral utilities. Ultimately the essay will address the possible defences that Wirral utilities may have against the action brought by the claimant. People have rights in law apart from those arising out of contract. These rights can be enforced by the injured party. When these rights are infringed by somebody out of which the person is injured, and a damage results out of that injury, the party whose act or omission led to the tort is liable to the person aggrieved. Law imposes a duty upon everybody to take care that no one suffers as a result of his act or omission. Thus, in law, a person is duty bound to keep his dog on a

Do we own our cells Essay Example | Topics and Well Written Essays - 500 words

Do we own our cells - Essay Example a consent form containing various details among them; description of the research, purpose, procedure description, feature risks, benefits and confidentiality. In addition, the practitioner while seeking the consent of the participant ought to have given further intentions behind the results (Leino-kilpi 11). These are economical benefits, which in reality were not for the patient but ended up using them and sought judicial intervention to contradict the entire case. This is not what it ought to be because the practitioner’s intention in this case was not to advance the field’s knowledge but more so to benefit economically without the knowledge of the patient. In addition, confidentiality is a basic principle supposed to be between the practitioner and the patient (Leino-kilpi 13). This is because the patient usually expects no one would access or obtain his or her information but with consent it may be helpful in other fields through distribution. Therefore, in this case the practitioner took advantage of the patient’s ignorance and a statute that avails freedom one to use own knowledge in benefitting economically (Leino-kilpi 11). Defining cell donors’ ownership right has been major stumbling block in determining the right balance between patient right and medical research progress. Every patient has right to privacy and information; hence any body part taken from them should not be done without their consent. If the cell is to be used for any scientific research or commercialization implications, the patient should at least get a share of the profit gained in such cases. In addition, the patient should avail well-versed consent as evident in Washington University v. catalona case (2006). In this case, I think body cells belong to the donor and practitioner cannot perform any act that infringes his or her right. This implies suppose researchers in anyway infringes any of the stated laws ought to withdraw his or her cells from the intended research

General concept of Human Rights Essay Example | Topics and Well Written Essays - 3500 words

General concept of Human Rights - Essay Example Both the European Convention on Human Rights and the Human Rights Act 1998 have as subjects human rights and fundamental freedoms. Human rights and freedoms are necessary and basic components of man's existence. It is not therefore an overstatement if one says that if he cannot have liberty, he will better have death. Life without freedom or life without human rights is not life at all. History is full of tales against suppression of human rights and fundamental freedoms. Hence, we have the story of the Israelites fleeing in exodus in the belief of savoring freedom in the end. We have the Tiananmen Square. We have the demolition of the Apartheid. Slavery has become a thing of the past. Servitude must always be voluntary. The concept of forced labor had long vanished in all civilized countries. Article 1 of the United Nations Universal Declaration of Human Rights very correctly stresses the affirmation that all human beings are born free. All the people of the world are equal in dignity and rights. Endowed with reason and conscience, men should act towards one and the other as brothers. Before putting forward the arguments and discussions which respectively pertain to the two commandment pillars of human rights and fundamental freedoms, it is logical, necessary and highly advisable that the underlying premises are first laid down. This methodology will lead all and sundry to have a better understanding of the basic fundamentals that gave rise to these historical enactments. In 1950,1 through the initiatives of the Council of Europe, the Convention for the Protection of Human Rights and Fundamental Freedoms was adopted with the end in view of giving protection to human rights and fundamental freedoms in Europe. It is also called the European Convention on Human Rights, referred to here as the Convention for brevity. The United Kingdom is a member state. As a necessary element and complement in order to establish the enforcement mechanisms of the Convention, the European Commission on Human Rights was created in 1954. This evolved into the European Court of Human Rights beginning 1959. The latter was put in place as a permanent tribunal on November 1, 1998 with full-fledged judges. It has its building and offices at Strasbourg, France.2 The Convention is effective in all states or territories which are members thereof or signatories thereto. The Convention therefore has a multi-national coverage or jurisdiction over those sovereignties and the individual citizens or residents thereof as far as concerning human rights and fundamental freedoms. States who are members of or signatories to the Convention have to go in line with the policies that it develops and promotes. In the case of the protocol on the death penalty, for instance, each member or signatory state has to abolish the capital punishment.3 Upon the other hand, the Human Rights Act 1998 is a law passed by the United Kingdom (UK) legislature in 1998. What is it about The prefatory of this legislation says, among others, that it is an act to give further effect to the rights and freedoms set forth in the Convention. Is not the Human Rights Act 1998 (or Act for short) a duplication of the Convention or an encroachment upon the latter Definitely, it

The similarities and differences about buddhism between China and Research Paper

The similarities and differences about buddhism between China and Japan - Research Paper Example idual choice for a particular religion is not one unique aspect as there are several influences that make one follow the belief of their ancestors by default. It is only when one is mature enough to think and ponder upon the teachings that he either becomes more devout in his beliefs or seeks new religions to satisfy his thirst for a solid system of principles that he deems supportive and moral. Buddhism is one of the five most followed religions of the world. The concept revolves around the attainment of a state of satisfaction or nirvana, through spiritual development, meditation and acting upon principles of morality and wisdom. The word Buddhism is composed of the word called ‘budhi’ that means enlightenment or awakening. This signifies the origin of Buddhism as the awakening of Siddhartha Gotama’s mind at the age of 35, some 2500 years back. This man is now known as Buddha and his idols are paid respects by the followers of Buddhism worldwide. The concept of a creator or a God is not included as the teachings of this religion, thus is does not revolve around strengthening the bond between the creator and the humans. On the contrary, it believes that change is inevitable and in order for a person to attain a state of spiritual satisfaction he has to act morally and follow the teachings that are designed to help him live through his lifelonguncertaint ies without being wavered. The concept of Buddhism basically starts off with the journey of Buddha and his path of enlightenment. Buddha was born in a royal family in today’s Nepal 2500 years ago. Not having seen any suffering or discomfort in his life, he lived peacefully within his family’s royal enclosure. However, once he stepped out into the real word he saw scenes that were unknown to him, that of poverty, suffering and the harsh realities of life. This incident made him set out to find a balanced way of life that was fair and neither of the two extremes of poverty and luxury. He was resting during

Implementation of an Integral ERP System Essay Example | Topics and Well Written Essays - 2000 words

Implementation of an Integral ERP System - Essay Example All these tasks have to performed on the real-time basis. Inventory control is one of the key areas, where a lot of companies claims to have saved the lot of money, which otherwise spent on manual inventory. Â  Here, an attempt has been made to understand the ERP solution, being used at Actavis, which is my SCM from the SAP. The system seems to be slightly low performing at the real-time, so a recommendation to adopt PeopleSoft Enterprise available from Oracle has been made. Advantages of the new system have been elaborated. Justification of change and a complete action plan has been prepared. A cost reduction, while using this system has been anticipated, mostly from inventory control. Â  2.0 Introduction: The changes over the past decade have expanded the business of many Fortune 500 and International companies. ERP had made an integral element within these large organizations. The modern ERP system performs "what if" analyzes for various processes, track human resource information, manage warehouses and allow sophisticated analysis and tracking of customer buying habits and preferences. These modern ERP systems are capable to track, monitor and share data across multiple locations (Karl et al, 2001, p. 22). Â  quality pharmaceutical bus... Â  e (About Actavis n.d.) and uses a Supply Chain Management (SCM) package of SAP Germany, to monitor, supply chain management, inventory management and sales records. Although the system is effective it is very low while executing. Actavis, being multinational is advised to adopt an ERP package from Oracle for improving supply chain management, throughout the globe. 3.0 Description of the current situation: At present mySCM from SAP is being used at the local location of the company Actavis. A day-to-day inventory management is being monitored and production is advised accordingly. To achieve a better production, supply, demand management, the following points need to be taken into consideration. Â  Production planning at all locations throughout the globe. Â  Consumption and demand patterns. Â  Supplying the product according to demand from one of the manufacturing location to obtain cost-effectiveness. Â  Distribution and customer services. Â  The current software is not able to deal with all the points as mentioned above, therefore in the current situation, there is a need to improve the supply chain management to achieve better results in sales, production in profits. Â   Â  

All about environmentalism Assignment Example | Topics and Well Written Essays - 750 words - 1

All about environmentalism - Assignment Example In this regard, this prejudice is relating environmentalism to racial, sexual, and economic discrimination; this has pushed some environmentalists to protect the rights of all the people to expand the entire ecosystem. Environmental history regards moral code is revolutionary and has caused theatrical development of the human thought. On the other hand, this discrimination was being driven forward by a certain group of individual who was enjoying the fruits from the contradiction of ethics to other groups. However, the science of ecology and diffusion into dispersal traditional eagerness, and biology came up with new commencement for a moral community (Nash, 1989). Scientific concern on the subject of the quality of the environment and interrelated public health and ecological is continuing to be rigorous. The environmental health and fortification have gone ahead to request the public, media, and the political leaders to consider being acceptable (Mikhailovich, Morrison & Arabena, 2007). In this regard, the environmental health and protection has sustained to spread out and become multifarious, they come up with programs based on managerial structure. In the same context, peculiarities are synthetic and have pushed to unsuitable organizational bewilderment, unwanted programmatic gaps and overlaps, and different activities have shared the common objective of guiding the public‘s health and ensuring environmental quality. However, in some cases, various terminologies have molded discordant administrative barriers rather than erecting organizational bridges amid the organizations involved in the fight back for environmental quality. In oth er environmental health and protection organization, programs are performed by agencies rather than the public health service (Webb & King, 2004). Moreover, environmental health and safety supervision is complicated causing challenges entailing both

Ideo Assignment Example | Topics and Well Written Essays - 500 words

Ideo - Assignment Example It proves that a liberal management system is necessary for a company to achieve continuous successful results (Neri, 2010). The company also invests in building great infrastructure for the designers allowing them to have fun creating and executing new ideas. The company has also come up with creative futuristic merchandises and solutions, which penetrate the market on a global scale. Among many other inventions IDEO has had, since inception, the redesigned shopping cart as one of its most successful inventions. The IDEO designers noticed problems with the traditional designs and changed them (Roth, 2011). Factors like safety when using the trolley were also a major issue. Additionally, inventing a product scanner to scan the goods reduces the time a customer spends at a till waiting for their turn to purchase. For example, instead of customers pushing trolleys around crowded supermarkets they can leave them at the end of an aisle and. It has not yet been released to the market, because of stiff competition particularly in the French markets (Roth, 2011). The 1998 IDEO shopping cart design was a success though it was not ready to enter to the market (Roth 2011). The firm had an excellent idea that would impress the customers and ease the efforts made in traditional way of shopping. However, the marketing team had not researched the market to find out if it would be economically viable, or if the retailers would accept the extra costs. As noted with products like the iPhone, clients are ready to purchase particularly if they are of good quality, are friendly and technologically enhanced. If I hired IDEO to redesign my products, I would be extremely confident with the firm. However, this would only be possible if I had a brilliant idea of a new product or the need to substantially improve on an existing one. I would be confident because I have seen that over time, the firm has invented products that are visionary,

Moral Panic Definition Essay Example for Free

Moral Panic Definition Essay Deborah Cameron is a linguist whose focus research is on what people’s attitudes are towards language. She writes a long definition on moral panic in Verbal Hygiene explaining how the media and general public exaggerate concerns beyond reason. Cameron reports that Jock Young describes moral panic as the public’s reaction that is â€Å"completely disproportionate to the actual problem.† Cameron explains that the causes of moral panic are analyzed in a simplistic manner, but the concern to the problem escalates to intolerable levels. She uses the term â€Å"folk devil† as an example of how they are identified in gang related violence and is a scape goat to the exaggerated issues reported by the media. Cameron also states from what scholars have suggested â€Å"that moral panic†¦is a product of modern mass media†¦Ã¢â‚¬ , if there is media attention the event will turn into an issue. However, if the media does not give attention, then the event will go unnoticed. In â€Å"American Werewolf in Kabul†¦Ã¢â‚¬  Sean Brayton, a Ph.D student researching the specifics of critical race theory and media studies, analyzes the concept of moral panic as being an important cause of the potential threat of national security to the United States of America. He illustrates the three main elements of moral panic: folk devils, ambiguous terms, and moral entrepreneurs using the reality of John Walker Lindh’s journey through multiple identities. Comparing Cameron’s definition of moral panic to Brayton’s discussion of moral panic, which originated from Cohen’s developed description of the context in 1972, there is agreement that media overemphasize concerns beyond practicality. Both Cameron and Brayton use the term â€Å"folk devils† to represent a subgroup of individuals that is a leading cause of moral panic, yet with different purposes. Cameron suggests that the term â€Å"folk devil† is usually branded to social minorities that bear the burden enmity and blame by the socially ideal majority, whereas Brayton expands Cohen’s understanding of the term as a threat to the moral constitution of society on the whole. Although their research areas are not of a similar context, they both relate their writing to a â€Å"cultural history† in an era of media induced politics. As the previous paragraphs mentioned, the term â€Å"moral panic† is applied in both Cameron and Brayton’s writing, which Cameron realizes the crucial influence to expanded reports, while Brayton blames that those reports magnify the guilty to the individuals who commit. According to Brayton, three essential elements can be found in the concept moral panic: folk devils, moral entrepreneur, and ambiguous terms. Those elements are perfectly applied to a real life example during WWII, most of the innocent Japanese-Americans (devil folks) were forced to move into the internment camp by the U.S.A. Government (moral entrepreneur) after American military base in Pearl Harbour was destroyed by Japanese army. The U.S.A. Government treated the Japanese-Americans unfairly, as national enemies, traitors, or spies for the ir homeland (defined terms). Cameron is a linguist and uses moral panic theory to explain why negative attitudes arose toward youth literacy in 1980 1990’s England. Brayton looks at moral panic theory from the perspective of cultural politics and how moral panic was used post 9/11 to preserve American ideals and create separation from conflicting cultural values. In both cases, Cameron and Brayton use moral panic theory to understand a culture’s reaction to some social problem exaggerated by the media. Moral panic theory provides researchers with a method of analyzing a situation resulting from a moral panic. Moral panic is, as Cameron describes, a problem â€Å"†¦discussed in an obsessive, moralistic and alarmist manner†¦Ã¢â‚¬ . The theory may also be a useful model for researchers dealing with the study of human behavior or culture, such as cultural history, social theory, criminology, and anthropology. In particular, it could be useful in studying the effects of media on culture.

Hydrogen Summary Essay Example for Free

Hydrogen Summary Essay * This is how hydrogen fuel cells work: 1. Gas stored in tanks 2. Atoms reach anode 3. Become hydrogen ion and a free electron 4. Ion goes through electrolyte layer 5. Hydrogen ion passes, but free electron does not 6. Free electron runs through external circuit from anode (-) to cathode (+) 7. Current of electrons creates electricity 8. Hydrogen ion enters cathode and combines with oxygen to become water which is better for the ecosystem because water vapor is not dangerous. * Why hydrogen as fuel? Efficient: not expensive to fill and it gives you more range. Emission Conscious: Hydrogen fuel cell cars release water vapor back in the atmosphere and don’t damage it by releasing C02 just like the other fuels. Fueling Up/Range: Since hydrogen is stored and highly compressed tanks it can hold more than any other fuel and has a bigger range. Global Economic Competitive Edge: Hydrogen is not as expensive as gasoline and it would cost you about  ½ of the money that you used to fill your car with gasoline to fill your car with hydrogen. * Better than other fuels: Hydrogen VS Ethanol: * Ethanol releases CO2 while hydrogen releases water vapor * Ethanol competes with the food producers(corn in the US, sugar cane in Brazil)and hydrogen doesn’t compete with anyone. * Ethanol is inefficient to produce while hydrogen is efficient. Hydrogen VS biodiesel: * Biodiesel solidifies in cold temperatures and it is harder to travel through the tubes(high viscosity) while hydrogen doesn’t solidify. * Biodiesel releases the most C02 out of all the fuels. Hydrogen releases water vapor * The range for biodiesel cars is 10% less than propanol cars . Hydrogen cars have 25% more range than biodiesel cars and 15% more than propanol cars. Hydrogen VS propanol: * Propanol has problems in high climates due to viscosity and the fuel solidifying while hydrogen cars don’t have problems with this. * Propanol releases C02 in the atmosphere, hydrogen releases water vapor. * The range for a propanol car is 15% less than a hydrogen car.

History of the Gun

History of the Gun The gun is a very unique piece of work that has all kinds of uses. The Chinese invented the gun many years ago. Since the gun has been invented it has improved tremendously and is still advancing this day. When was the gun invented? The first gun was invented in the year 1232. This invention was introduced after the invention of black powder was discovered. â€Å"Gunpowder is an explosive mixture of 15% charcoal, 10% sulfur, and 75% potassium nitrate, or saltpeter.†1 Gunpowder was used for fireworks that was fired out of bamboo sticks during the ninth century. The bamboo stick was also used as the first gun, they were not very productive because they were so brittle, but they were used to try to stop the Mongol invaders. â€Å"Europeans obtained gunpowder in the thirteenth century.†2 The Europeans took the recipe to this mixture and was going to enhance the gun severely. The first type of firearm invented by the Europeans was the cannon; the cannon was used to siege the defenders in the castle walls. The cannonballs fired from the cannons would crumble the castle walls leading to the end of feudalism. The first siege that the Europeans conquered was the siege of Metz in the year 1324. â€Å"Cannons were very effective weapons in a siege, but soldiers soon wanted guns they could carry. At first, simple â€Å"hand gonnes† were used side-by-side with traditional weapons such as crossbows, pikes, and lances. The development of small arms quickly changed how military battles were fought.†3 The knights that fought on the front line of the military were soon defeated when the gun was invented. The armor could withstand swords, spears and lances, but when a bullet was shot it pierced through the armor making it defenseless against the gun. When the full armor body suits were put away the helmets and the breastplates were introduced. The breastplates were made out of very hard steel and could withstand a straight shot from a bullet. This invention gave each side a chance to survive from a gunshot. Starting around the 1400s blacksmiths began inventing more and easier ways to operate a gun. The first invention that simplified the gun was the matchlock gun. A wick was attached to a clamp that released into a chamber full of gunpowder. This cut reload time down a little but not much. During the 15th and 16th century the only thing that changed on the gun was the way they produced a spark to fire. The 18th century rolled around and a percussion cap gun was invented. The percussion cap gun was invented by a man named Reverend John Forsyth. â€Å"firing mechanism no longer uses flash pan, a tube lead straight into the gun barrel, the tube had an exposed cap on it that exploded when struck†4 During the 18th century there were all sorts of guns invented. Guns ranging from revolvers and center firing guns all the way to shotguns and rifles. The automatic gun was even invented in the 18th century. During the 19th century a new gun was invented: the new gun was an automatic and it was a Winchester. The Winchester automatic rifle was invented during the year 1903. More rifles were invented during the 19th century and the more they were invented the more sophisticated and enhanced they became. Guns anywhere from the Tommy gun to the Assault rifle. When these guns were invented they were able to kill lots of people in a short period of time. The time it takes to reload a gun today verses the time it took 3 centuries ago has changed dramatically. Centuries ago it took almost two minutes to reload and fire a gun. Today you can shoot up to a thousand rounds a minute. Mankind has invented guns so that they can be used to kill people in mass numbers. When ships were introduced to guns they took advantage of opportunity to use them. Sailors could use guns to rob other boats and take over villages and tribes. Even though the people that lived in the tribe would outnumber the people on the boat, they could be conquered by something that they have never seen before in their life. Why were guns invented? â€Å"Guns were invented not for protection against the elements or for sport or for hunting but with the simple purpose to fight other men.†5 There was a man named Samuel Colt that quoted the phrase: â€Å"God made man. Samuel Colt made them equal†6,this is said to be true because of what Samuel Colt invented. Samuel Colt invented the revolver, which is still used in todays society in some countries. With the idea that the gun was invented to fight other men the world has turned into a war. Everywhere that you turn and look you will see someone with a gun or is being robbed by someone with a gun. Some people that own guns dont think before they act. This is why there are so many murders with guns. Everyone in the military has a gun issued to them. Most of the military carry machine guns with them into combat for the simple reason that the machine gun can fire rapid fire for a long period of time. All you have to do with a machine gun is keep it cool and keep the gun lo aded at all times and shooting will never stop. The man who invented the machine gun is Doctor Richard Gatling. â€Å"Doctor Richard Gatling patented his design of the â€Å"Gatling Gun†, a six-barreled weapon capable of firing a (then) phenomenal 200 rounds per minute.†7 The Gatling gun was invented in the year 1861. During the year 1885 The Maxim Machine Gun was invented. Years later the Tommy gun was invented. â€Å"The Thompson submachine gun or Tommy gun was invented by General John T. Thompson, it was the first hand held machine gun. Thompson was driven with the thought of creating a hand held machine gun that would help end the First World War, However, â€Å"the first shipment of prototype guns destined for Europe arrived at the docks in New York city on November 11, 1918, the day the war ended.†8 New and Improved Guns The guns of the 20th century are amazing. They have guns that can see around corners and guns that tell you how far your target is away from you. The equipment that mankind has come up with is far more advanced than anyone has ever thought. We now have radars that can see where you are at night. The infrared radar system has let us see things at night. This helps the military find the enemy in tough weather such as rain or snow, but the infrared radar has one downfall. The radar system cannot see anything submerged in water. Next we have the smart bullet, the smart bullet can follow your every turn, and you cant hide from it. The distance that the bullet can travel depends on where the target is. â€Å"According to recently declassified research by the Department of Defense, the new bullets will allow snipers to hit targets several kilometers away.†9 The smart bullet travels at Mach 3. The reason that this bullet can maneuver like it does is because it have a ball joint that c onnects the nose of the bullet to the casing. â€Å"The nose can move by up to 0.1 degrees in any direction.†10 The gun that this bullet comes out of has to be very powerful to make this bullet travel the distance it does. The military is going from guns to chemical warfare now. Chemical warfare is very dangerous to use because it spreads and doesnt stop and is hard to control. â€Å"Chemical warfare is warfare (and associated military operations) using the toxic properties of chemical substances to kill, injure, or incapacitate an enemy.†11 Chemical weapons are and can be very dangerous and have been used since the 1900s. The way that you use to tell how chemicals were surrounding you was by smell, by the time u smelled the gas you were dead, you had no chance of living. The chemical warfare today has improved its detection. The way that you can tell the chemicals are around you now are by chemical strips, laser detection, alarms, and blister agent detectors. There ar e three schedules of chemical weapons that can be used for warfare. The first schedule has little use. This schedule is mainly for medical research and pharmaceutical use. â€Å"Examples include nerve agents, ricin, lewisite, and mustard gas.†12 The second schedule has no big industrial uses but is used legitimately for small uses. â€Å"Examples include dimethyl methylphosphonate, a precursor to sarin but also used as a flame retardant and thiodiglycol, a precursor in the manufacture of mustard gas but also widely used as a solvent in inks.†13 The last scheduled substance has large-scale industrial uses. â€Å"Examples include phosgene and chloropicrin.†14 Both of these chemicals have been in use when it comes to chemical warfare. The substance phosgene is used in the production of plastic. The chemical Chloropicrin is used as a fumigant. If both of these plants produce up to 30 tons a year they have to be reported to The Organization for the Prohibition of Che mical Weapons, (OPCW). Guns have taken a very big toll on the 20th century population. The improvement of guns will never stop and will keep on getting more sophisticated. History is always in the making and history is always repeating itself, so keep up with time or it will leave you standing alone! Notes 1. Thomas Gale. Bookrags.com The Invention of Guns. Science and Its Times: 700-1449. (Background) 1st paragraph, 1st sentence. 2. Bookrags.com (Background) 2nd paragraph, 1st sentence. 3. Bookrags.com (Impact) 2nd paragraph, 1st 3 sentences. 4. Mary Bellis, About.com. History of Firearms. (Timeline), Year 1825. 2nd part of sentence. 5. Blurtit. Why Were Guns Invented? 1st paragraph, Last sentence. 6. Blurtit. 1st paragraph, 2nd sentence. 7. Mary Bellis, About.com. The History of Guns Rifles and Machine Guns. (Machine Guns- Gatling Gun-1861) 1st sentence. 8. (Machine Guns- Thompson Submachine Gun Tommy Gun) 1st 2 sentences. 9. Justin Mullins-New Scientist. Sniper Country.com. 1st paragraph. 2nd sentence. 10. Sniper Country.com. 3rd paragraph, Last sentence. 11. New World Encyclopedia. Chemical Warfare. 1st paragraph, 1st sentence. 12. New World Encyclopedia. (Three groups of chemical weapons Schedule 1 substances). 3rd sentence. 13. New World Encyclopedia. (Three groups of chemical weapons Schedule 2 substances). 2nd sentence. 14. New World Encyclopedia. (Three groups of chemical weapons Schedule 3 substances). 2nd sentence. Bibliography Blurtit. Why Were Guns Invented? The New York Times Company. Revised 2009. November 11 2009. http://inventors.about.com/od/militaryhistoryinventions/a/firearms.htm CALVO, SHERRI CHASIN. The Invention of Guns. 2005. November 11 2009. . Mullins, Justin New Scientist. â€Å"You can run, but you cant hide†.[Archive: 12 April 1997] November 11 2009 http://www.snipercountry.com/Articles/SmartBullets.asp Chemical warfare. New World Encyclopedia. 14 Jan 2009, 19:22 UTC. 18 Nov 2009, 07:21 .