Mini-batch Tempered MCMC
Dangna Li, Wing H Wong

TL;DR
This paper introduces Mini-batch Tempered MCMC (MINT-MCMC), a scalable method for Bayesian inference that efficiently explores multi-modal posteriors using mini-batch data and a novel parallel sampling algorithm.
Contribution
It proposes a new framework for mini-batch MCMC that estimates the Metropolis-Hastings ratio from data subsets and develops a parallel algorithm based on the Equi-Energy sampler.
Findings
Efficiently explores multiple modes of complex posteriors.
Samples from the true posterior raised to a known temperature.
Demonstrates scalability and effectiveness in high-dimensional settings.
Abstract
In this paper we propose a general framework of performing MCMC with only a mini-batch of data. We show by estimating the Metropolis-Hasting ratio with only a mini-batch of data, one is essentially sampling from the true posterior raised to a known temperature. We show by experiments that our method, Mini-batch Tempered MCMC (MINT-MCMC), can efficiently explore multiple modes of a posterior distribution. Based on the Equi-Energy sampler (Kou et al. 2006), we developed a new parallel MCMC algorithm based on the Equi-Energy sampler, which enables efficient sampling from high-dimensional multi-modal posteriors with well separated modes.
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Taxonomy
TopicsUnderwater Acoustics Research · Random lasers and scattering media · Nanopore and Nanochannel Transport Studies
