01877aam a2200205 4500999001900000008004500019020001800064082002100082100002900103245004700132260003000179300002600209520111200235650002401347650002301371650003201394650004901426942001201475952018401487 c211464d211464190326b 2018 ||||| |||| 00| 0 eng d a9781482238068 a519.542bW2M2 aWatanabe, Sumio 9376910 aMathematical theory of bayesian statistics bCRC Pressc2018aNew York aix, 319p.bWith index aMathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which both the generalization loss and the marginal likelihood are estimated even if the posterior distribution cannot be approximated by any normal distribution.Explains Bayesian inference not subjectively but objectively. Provides a mathematical framework for conventional Bayesian theorems. Introduces and proves new theorems.
Cross validation and information criteria of Bayesian statistics are studied from the mathematical point of view. Illustrates applications to several statistical problems, for example, model selection, hyper parameter optimization, and hypothesis tests.This book provides basic introductions for students, researchers, and users of Bayesian statistics, as well as applied mathematicians.
https://www.crcpress.com/Mathematical-Theory-of-Bayesian-Statistics/Watanabe/p/book/9781482238068 aMathematics9376911 aStatistics9376912 aMathematical models9376913 aBayesian statistical decision theory9376914 2ddccBK 00102ddc406519_000000000000000_542_W2M2708NFIC9357675aVSLbVSLcGENd2019-03-25e5g10.00kSlot 1677 (2 Floor, East Wing)o519.542 W2M2p198809r2019-03-25v12571.00yBK