Scalable Monte Carlo for Bayesian Learning (Hardcover) (ISBN-13: 9781009288446)

A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge 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. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.

Author: Paul Fearnhead, Christopher Nemeth, Chris J. Oates
ISBN-10: 100928844X
ISBN-13: 9781009288446
Publisher: Cambridge University Press
Language: English
Published: 06/05/2025
Pages: 247
Format: Hardcover
Weight: 1.11lbs
Size: 9.00h x 6.00w x 0.63d