Bayesian Model Selection and Statistical Modeling (Record no. 16115)

MARC details
000 -LEADER
fixed length control field 02201nam a2200289Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250923144844.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220909b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781439836149
037 ## - SOURCE OF ACQUISITION
Terms of availability Textual
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Language of cataloging eng
Transcribing agency CSL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng.
084 ## - COLON CLASSIFICATION NUMBER
Classification number B2871 Q0
Assigning agency CSL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ando, Tomohiro
Relator term author.
9 (RLIN) 829099
245 #0 - TITLE STATEMENT
Title Bayesian Model Selection and Statistical Modeling
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Suite :
Name of publisher, distributor, etc. CRC,
Date of publication, distribution, etc. 2010.
300 ## - PHYSICAL DESCRIPTION
Extent xiv, 286p.
490 ## - SERIES STATEMENT
Series statement Statistics : Textbooks and Monographs
500 ## - GENERAL NOTE
General note Includs Bibliography 265-284p.; Index 285-286p.
520 ## - SUMMARY, ETC.
Summary, etc. 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.<br/>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.<br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bayesian statistical decision theory.
9 (RLIN) 829101
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Mathematical models.
9 (RLIN) 829103
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Mathematical statistics.
9 (RLIN) 829104
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistics.
9 (RLIN) 829106
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Classification part B2871 Q0
Koha item type Textual
Source of classification or shelving scheme Colon Classification (CC)
Suppress in OPAC No
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        Central Science Library Central Science Library 2022-09-12 1733, 24/05/2012, Ashutosh Technical Books   B2871 Q0 SL1558429 2022-09-12 2022-09-12 Textual
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