Cambridge University Press Cambridge Scalable Monte Carlo for Bayesian Learning Book

Advance your understanding of Bayesian computation with this graduate-level text from Cambridge University Press. Part of the Institute of Mathematical Statistics Monographs, this book provides a comprehensive introduction to modern Markov chain Monte Carlo (MCMC) methods. It covers essential topics that have emerged over the last decade, making it a vital resource for those studying high-dimensional data and large-scale machine learning. Readers will explore cutting-edge techniques including stochastic gradient MCMC, non-reversible MCMC, and continuous time MCMC. The text also addresses new methods for convergence assessment, ensuring you stay current with recent developments in the field. Because the material focuses on scalability regarding data volume and dimensions, it is specifically designed to meet the needs of modern AI and machine learning applications. Practical examples are integrated throughout the chapters to demonstrate how these complex mathematical concepts function in real-world computational contexts.