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| 文件名: Bayesian Modeling Using WinBUGS.pdf | |
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bayesian modeling using wingbugs
书的简介 Product Description The BUGS (Bayesian inference Using GibbsSampling) project is concerned with free, flexible software for theBayesian analysisof complex statistical models using Markov ChainMonte Carlo (MCMC)methods. It details the various and commonly-usedmodeling techniquesthat are employed by statisticians in a multitudeof sciences such as;biostatistics and social science; actuarialscience environments. Thisbook presents the reader with a clear andeasily accessible introductionto theuse of WinBUGS programmingtechniques in a variety of Bayesian modelingsettings. Emphasis isgiven to Generalized Linear Models (GLMs) familiarto most readers andresearchers. Detailed explanations cover modelbuilding, priorspecification, writing WinBUGS code, and analyzing andinterpretingWinBUGS output. Also features comprehensive problems andexamples. From the Back Cover A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGSprovidesan easily accessible introduction to the use of WinBUGSprogrammingtechniques in a variety of Bayesian modeling settings. Theauthorprovides an accessible treatment of the topic, offering readersa smoothintroduction to the principles of Bayesian modeling withdetailedguidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference andtheWinBUGS software and goes on to cover key topics, including:
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGSserves as an excellentbook for courses on Bayesian statistics at theupper-undergraduate andgraduate levels. It is also a valuablereference for researchers andpractitioners in the fields ofstatistics, actuarial science, medicine,and the social sciences whouse WinBUGS in their everyday work. 网络上的一个用户评价 I highly recommend this book to anyone interested in Bayesianstatisticsand the WinBUGS software. Examples are excellent and clear,even theexamples of code, which are well-commented. If I had to pickat it,there are a few typos, but then again, I got the first printing,andthey are noted on the author's website. I have read many Bayesianbooks,and if you are new, I think this is one of the best. 希望大家喜欢和支持,谢谢 |
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