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Computational Bayesian Statistics
M. Antónia Amaral Turkman
Carlos Daniel Paulino
Peter Müller
出版
Cambridge University Press
, 2019-02-28
主題
Business & Economics / Statistics
Computers / Artificial Intelligence / General
Computers / Artificial Intelligence / Natural Language Processing
Language Arts & Disciplines / Library & Information Science / General
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Bayesian Analysis
ISBN
1108481035
9781108481038
URL
http://books.google.com.hk/books?id=d4KFDwAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
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.