Computational Bayesian Statistics: An Introduction

M. Antonia Amaral Turkman (Universidade de Lisboa),Carlos Daniel Paulino (Universidade de Lisboa),Peter Muller (University of Texas, Austin)

Computational Bayesian Statistics: An Introduction
Format
Hardback
Publisher
Cambridge University Press
Country
United Kingdom
Published
28 February 2019
Pages
254
ISBN
9781108481038

Computational Bayesian Statistics: An Introduction

M. Antonia Amaral Turkman (Universidade de Lisboa),Carlos Daniel Paulino (Universidade de Lisboa),Peter Muller (University of Texas, Austin)

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.

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