| 000 | 02694nam a2200313 a 4500 | ||
|---|---|---|---|
| 001 | vtls000056379 | ||
| 003 | KUKTEM | ||
| 005 | 20251125093045.0 | ||
| 008 | 111130t2010 flua f 001 0 eng d | ||
| 020 | _a9781439836149 (hc : alk. paper) | ||
| 020 | _a1439836140 (hc : alk. paper) | ||
| 039 | 9 |
_a201206191046 _basmadi _y201111301100 _zsri |
|
| 040 | _aUMP | ||
| 090 | _aQA279.5 .A53 2010 | ||
| 100 | 1 | _aAndo, Tomohiro | |
| 245 | 1 | 0 |
_aBayesian model selection and statistical modeling / _cTomohiro Ando |
| 260 |
_aBoca Raton, FL : _bCRC Press, _c2010 |
||
| 300 |
_axiv, 286 p. : _bill. ; _c25 cm. |
||
| 490 | 1 | _aStatistics : textbooks and monographs | |
| 504 | _aIncludes bibliographical references and index | ||
| 505 | 0 | _aIntroduction to Bayesian analysis -- Asymptotic approach for Bayesian inference -- Computational approach for Bayesian inference -- Bayesian approach for model selection -- Simulation approach for computing the marginal likelihood -- Various Bayesian model selection criteria --Theoretical development and comparisons -- Bayesian model averaging | |
| 520 | _a"Along 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."--Publisher’s description | ||
| 650 | 0 | _aBayesian statistical decision theory | |
| 650 | 0 | _aMathematical statistics | |
| 650 | 0 | _aMathematical models | |
| 830 | 0 | _aStatistics : textbooks and monographs | |
| 999 |
_aVIRTUA40 _c60968 _d60974 |
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| 942 | 0 | 0 | _06 |