Computational Bayesian Statistics: An Introduction

Turkman, M. Antonia Amaral/ Paulino, Carlos Daniel/ Muller, Peter

ISBN 10: 1108481035 / ISBN 13: 9781108481038

Published by Cambridge Univ Pr, 2019

About this Item

243 pages. 9.00x6.00x0.75 inches. In Stock. Seller Inventory # x-1108481035

Bibliographic Details

Title: Computational Bayesian Statistics: An ...

Publisher: Cambridge Univ Pr

Publication Date: 2019

Binding: Hardcover

Condition: Brand New

About this title

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 Description:

This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user's guide for researchers and graduate students from beyond statistics.