  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.
