MCMC-driven learning

arXiv:2402.09598v1 Announce Type: cross
Abstract: This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includes black-box variational inference, adaptive MCMC, normalizing flow construction and transport-assisted MCMC, surrogate-likelihood MCMC, coreset construction for MCMC with big data, Markov chain gradient descent, Markovian score climbing, and more$\unicode{x2014}$within one common framework. By doing so, …

abstract arxiv box construction cs.lg flow inference intersection likelihood machine machine learning markov math.st mcmc paper stat.co stat.ml stat.th transport type unicode