000			02201nam a2200289Ia 4500
003			OSt
005			20250923144844.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781439836149
037			_cTextual
040			_aCSL _beng _cCSL
041			_aeng.
084			_aB2871 Q0 _qCSL
100			_aAndo, Tomohiro _eauthor. _9829099
245		0	_aBayesian Model Selection and Statistical Modeling
260			_aSuite : _bCRC, _c2010.
300			_axiv, 286p.
490			_aStatistics : Textbooks and Monographs
500			_aIncluds Bibliography 265-284p.; Index 285-286p.
520			_aAlong with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.
650			_aBayesian statistical decision theory. _9829101
650			_aMathematical models. _9829103
650			_aMathematical statistics. _9829104
650			_aStatistics. _9829106
942			_hB2871 Q0 _cTEXL _2CC _n0
999			_c16115 _d16115