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Paperback
Paperback
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata’s data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book''s content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata’s data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book''s content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
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