| 書目名稱 | Tools for Statistical Inference |
| 副標(biāo)題 | Methods for the Expl |
| 編輯 | Martin A. Tanner |
| 視頻video | http://file.papertrans.cn/926/925991/925991.mp4 |
| 叢書名稱 | Springer Series in Statistics |
| 圖書封面 |  |
| 描述 | This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. In this second edition, I have attempted to expand the treatment of many of the techniques dis- cussed, as well as include important topics such as the Metropolis algorithm and methods for assessing the convergence of a Markov chain algorithm. Prerequisites for this book include an understanding of mathematical statistics at the level of Bickel and Doksum (1977), some understanding of the Bayesian approach as in Box and Tiao (1973), experience with condi- tional inference at the level of Cox and Snell (1989) and exposure to statistical models as found in McCullagh and Neider (1989). I have chosen not to present the proofs of convergence or rates of convergence since these proofs may require substantial background in Markov chain theory which is beyond the scope ofthis book. However, references to these proofs are given. There has been an explosion of papers in the area of Markov chain Monte Carlo in the last five years. I have attempted to identify key references - though due to the volatility of the field some work may have been missed. |
| 出版日期 | Textbook 19932nd edition |
| 關(guān)鍵詞 | Area; Bayesian inference; Gibbs sampler; Likelihood; Monte Carlo method; Statistica; algorithms; computatio |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-1-4684-0192-9 |
| isbn_ebook | 978-1-4684-0192-9Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York, Inc. 1993 |
|Archiver|手機版|小黑屋|
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