Bayesian Methods for Data Analysis
by Bradley P. CarlinBroadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis.
Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.
New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling?now commonplace in Bayesian data analysisA new chapter on Bayesian design that emphasizes Bayesian clinical trialsA completely revised and expanded section on ranking and histogram estimationA new case study on infectious disease modeling and the 1918 flu epidemicA solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem?available both electronically and in printIdeal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
- First published
- 2008
- Publishers
- Taylor & Francis Group
This is a good introductory book on Bayesian statistics for those looking to dive in. It comes with a host of online tools (datasets, codes, and examples) that are useful but MCMC computing is primarily based on the outdated (but functional) open-sourced R-based WinBugs, which was discontinued but free for all to use.
It's a nice book but i still don't understand why everyone in my department buys it.
The book finds a nice balance between theory and practice. I especially enjoyed how well it was structured.
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