M. Antonia Amaral Turkman

Computational Bayesian Statistics (eBook, ePUB)

An Introduction

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 28. Februar 2019
• Englisch
• ISBN-13: 9781108574617
• Artikelnr.: 55335582

Autorenporträt

M. Antónia Amaral Turkman was, until 2013, full-time Professor in the Department of Statistics and Operations Research, Faculty of Sciences, University of Lisbon. Though retired from the university, she is still a member of its Center of Statistics and Applications, where she held the position of scientific coordinator until 2017. Her research interests are Bayesian statistics, medical and environmental statistics, and spatiotemporal modeling, with recent publications on computational methods in Bayesian statistics, with an emphasis on applications in health and forest fires. She has served as vice president of the Portuguese Statistical Society. She has taught courses on Bayesian statistics and computational statistics, among many others.

Inhaltsangabe

1. Bayesian inference
2. Representation of prior information
3. Bayesian inference in basic problems
4. Inference by Monte Carlo methods
5. Model assessment
6. Markov chain Monte Carlo methods
7. Model selection and transdimensional MCMC
8. Methods based on analytic approximations
9. Software.