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Full Description
Marking a pivotal moment in the evolution of Bayesian inference, the third edition of this seminal textbook on Markov Chain Monte Carlo (MCMC) methods reflects the profound transformations in both the field of Statistics and the broader landscape of data science over the past two decades. Building on the foundations laid by its first two editions, this updated volume addresses the challenges posed by modern datasets, which now span millions or even billions of observations and high-dimensional parameter spaces. While faster, approximate methods have gained traction, MCMC remains the gold standard for rigorous and reliable Bayesian inference, and this book continues to champion its relevance in the face of evolving computational paradigms.
This edition introduces significant updates and expansions, including new material on infinite-dimensional MCMC, sequential Monte Carlo methods, and adaptive algorithms. It also revisits foundational topics with fresh insights, such as iterative dynamics, mixture distributions, and data augmentation, while incorporating cutting-edge developments like Hamiltonian Monte Carlo and Dirichlet process-based methods. With a focus on both theoretical rigor and practical application, the book equips readers to navigate the complexities of modern Bayesian modeling and computation.
Features:
Expanded coverage of sequential Monte Carlo methods, complementing MCMC with probabilistic foundations
A brand-new chapter on infinite-dimensional MCMC, addressing advanced stochastic simulation techniques for modern Bayesian modeling
Enhanced theoretical treatment of Markov chains on continuous state spaces, including nonhomogeneous Markov chains and adaptive algorithms
New sections on mixture distributions and data augmentation, showcasing their power in simplifying and improving MCMC algorithms
Detailed exploration of Hamiltonian Monte Carlo and Dirichlet process-based methods, reflecting recent advances in high-dimensional and scalable MCMC techniques
Completely revised software section, aligning with contemporary Bayesian computation practices and tools, with accompanying R and Python codes available on GitHub
This textbook is an essential resource for statisticians, data scientists, and researchers in fields such as machine learning, artificial intelligence, and computational biology who rely on Bayesian inference for analyzing complex, high-dimensional datasets. It is equally valuable for graduate students and academics seeking a comprehensive introduction to MCMC methods, as well as practitioners looking to deepen their understanding of modern Bayesian computation. With its blend of theoretical depth and practical guidance, this third edition serves as both a foundational text and a reference for advanced applications in the ever-expanding domain of Bayesian analysis.
Contents
1 Stochastic simulation. 2 Bayesian inference. 3 Approximate methods of inference. 4 Markov chains. 5 Gibbs sampling. 6 Metropolis-Hastings algorithms. 7 Further topics in MCMC. 8 Infinite-dimensional MCMC.
