The BUGS book : a practical introduction to Bayesian analysis
著者
書誌事項
The BUGS book : a practical introduction to Bayesian analysis
（Texts in statistical science）（A Chapman & Hall book）
CRC Press, c2013
- : pbk
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注記
Includes bibliographical references and index
Other editors: Christopher Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter
内容説明・目次
内容説明
Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines.
The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions-all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions.
More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas.
Full code and data for examples, exercises, and some solutions can be found on the book's website.
目次
Introduction: Probability and Parameters. Monte Carlo Simulations using BUGS. Introduction to Bayesian Inference. Introduction to Markov Chain Monte Carlo Methods. Prior Distributions. Regression Models. Categorical Data. Model Checking and Comparison. Issues in Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices. Bibliography. Index.
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