Computational Bayesian Statistics - An Introduction (Paperback)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Institute of Mathematical Statistics Textbooks
Release date:	February 2019
Authors:	M. Antonia Amaral Turkman • Carlos Daniel Paulino • Peter Muller
Dimensions:	227 x 152 x 13mm (L x W x T)
Format:	Paperback - Trade
Pages:	254
ISBN-13:	978-1-108-70374-1
Categories:	Books > Reference & Interdisciplinary > Communication studies > Data analysis Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Econometrics > Economic statistics Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-108-70374-7
Barcode:	9781108703741

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