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Computational Bayesian Statistics: An Introduction (Institute of Mathematical Statistics Textbooks)
M. AntÓNia Amaral Turkman; Carlos Daniel Paulino; Peter MÜLler (Author)
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Cambridge University Press
· Hardcover
Computational Bayesian Statistics: An Introduction (Institute of Mathematical Statistics Textbooks) - M. AntÓNia Amaral Turkman; Carlos Daniel Paulino; Peter MÜLler
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Origin: Spain
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Synopsis "Computational Bayesian Statistics: An Introduction (Institute of Mathematical Statistics Textbooks)"
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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
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