Computational Bayesian Statistics - Couverture souple

Amaral Turkman, M. Antónia

Couverture souple

ISBN 10 : 1108703747 ISBN 13 : 9781108703741

Editeur : Cambridge University Press, 2019

A propos du livre

Revue de presse

'An introduction to computational Bayesian statistics cooked to perfection, with the right mix of ingredients, from the spirited defense of the Bayesian approach, to the description of the tools of the Bayesian trade, to a definitely broad and very much up-to-date presentation of Monte Carlo and Laplace approximation methods, to a helpful description of the most common software. And spiced up with critical perspectives on some common practices and a healthy focus on model assessment and model selection. Highly recommended on the menu of Bayesian textbooks!' Christian Robert, Université de Paris IX, Paris-Dauphine, and University of Warwick

'This book aims to be a concise introduction to modern computational Bayesian statistics, and it certainly succeeds! The authors carefully introduce every main technique that is around and demonstrate its use with the appropriate software. Additionally, the book contains a readable introduction to Bayesian methods, and brings the reader up to speed within the field in no time!' Håvard Rue, King Abdullah University of Science and Technology, Saudi Arabia

Présentation de l'éditeur

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.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2019
• ISBN 10: 1108703747
• ISBN 13: 9781108703741
• Reliure: Broché
• Langue: anglais
• Nombre de pages: 256