Computational Bayesian Statistics: An Introduction

Amaral Turkman, M. Antonia, Paulino, Carlos Daniel, Muller, Peter

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商品描述

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.

商品描述(中文翻譯)

高效運用先進的貝葉斯方法需要對基礎知識有深入的理解。這本引人入勝的書解釋了構建和分析貝葉斯模型的基本思想,特別關注計算方法和方案。該書的獨特之處在於廣泛討論了可用的軟件包,並簡要但完整且數學嚴謹地介紹了貝葉斯推斷。該書介紹了蒙特卡羅方法、馬爾可夫鏈蒙特卡羅方法和貝葉斯軟件,並附有模型驗證和比較、跨維度MCMC和條件高斯模型的額外材料。書中的問題使其適合作為第一個研究生級別的貝葉斯計算課程的教科書,重點是蒙特卡羅方法。對於統計學以外的研究人員和研究生,該書廣泛討論了貝葉斯軟件-R/R-INLA、OpenBUGS、JAGS、STAN和BayesX,也很有用。