Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (Chapman & Hall/CRC Texts in Statistical Science)

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