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.

有意义地使用先进的贝叶斯方法需要对基本原理有良好的理解。这本引人入胜的书解释了支撑贝叶斯模型的构建和分析的思想，特别关注计算方法和方案。本书的独特之处在于对现有软件包的广泛讨论与对贝叶斯推断的简短但完整且数学上严谨的介绍相结合。该文介绍了蒙特卡洛方法、马尔科夫链蒙特卡洛方法和贝叶斯软件，还有关于模型验证和比较、跨维MCMC和条件高斯模型的补充材料。书中包含的问题使本书适合作为贝叶斯计算的第一个研究生水平课程的教科书，重点是蒙特卡洛方法。对贝叶斯软件的广泛讨论--R/R-INLA、OpenBUGS、JAGS、STAN和BayesX--使其对统计学以外的研究人员和研究生也很有用。