TY  - BOOK
AU  - Turkman,Maria Antónia Amaral
AU  - Paulino,Carlos Daniel
AU  - Müller,Peter
TI  - Computational Bayesian statistics: an introduction 
T2  - Institute of Mathematical Statistics textbooks
SN  - 9781108646185 (ebook)
AV  - QA279.5 .T87 2019
U1  - 519.542 23
PY  - 2019///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Bayesian statistical decision theory
KW  - Textbooks
N1  - Title from publisher's bibliographic system (viewed on 20 Feb 2019)
N2  - 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
UR  - https://doi.org/10.1017/9781108646185
ER  -