Computational Bayesian Statistics: An Introduction (Institute of Mathematical Statistics Textbooks #11)

By: M. Antónia Amaral Turkman and Carlos Daniel Paulino and Peter Müller

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Synopsis: Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Book Quality:: Publisher Quality
ISBN-13:: 9781108574617
Related ISBNs:: 9781108481038, 9781108481038
Publisher:: Cambridge University Press
Date of Addition:: 02/19/19
Copyrighted By:: M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.