Scalable Monte Carlo for Bayesian Learning
It offers a comprehensive overview of recent MCMC advancements for scalable Bayesian learning, primarily serving as an educational resource rather than presenting new research.
This book provides a graduate-level introduction to advanced Markov chain Monte Carlo (MCMC) algorithms for Bayesian computation, focusing on methods that are scalable with respect to data amount or dimension to address high-priority applications in machine learning and AI.
This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI.