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Probability and Bayesian modeling (Record no. 565400)

000 -LEADER
fixed length control field 02890 a2200265 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220216110640.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220214b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781138492561
040 ## - CATALOGING SOURCE
Transcribing agency IIT Kanpur
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.542
Item number Al14p
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Albert, Jim
245 ## - TITLE STATEMENT
Title Probability and Bayesian modeling
Statement of responsibility, etc Jim Albert and Jingchen Hu
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher CRC Press
Year of publication 2020
Place of publication Boca Raton
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiv, 537p
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman & HAll/CRC Texts in statistical science series
490 ## - SERIES STATEMENT
Series statement / edited by Joseph K. Blitzstein ...[et al.]
500 ## - GENERAL NOTE
General note A Chapman & Hall Book
520 ## - SUMMARY, ETC.
Summary, etc Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research.

This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection.

The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book.

A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Probabilities
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Bayesian statistical decision theory
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Hu, Jingchen
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Date acquired Source of acquisition Cost, normal purchase price Full call number Accession Number Cost, replacement price Koha item type
        General Stacks PK Kelkar Library, IIT Kanpur PK Kelkar Library, IIT Kanpur 2022-02-28 68 6029.80 519.542 Al14p A185625 7537.24 Books

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