Scalable Monte Carlo for Bayesian Learning

Paul Fearnhead; Christopher Nemeth; Chris J. Oates; Chris Sherlock

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arXiv · 2407.12751

Scalable Monte Carlo for Bayesian Learning

Paul Fearnhead·Christopher Nemeth·Chris J. Oates·Chris Sherlock

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Abstract

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.

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BibTeX RIS

Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock. 2024-07-17. Scalable Monte Carlo for Bayesian Learning. https://arxiv.org/abs/2407.12751

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