Generalized Parallel Tempering on Bayesian Inverse Problems

Abstract

In the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found its origins in the molecular dynamics community, and can be understood as the limit case of the continuous-time Parallel Tempering algorithm, where the (random) time between swaps of states between two parallel chains goes to zero. Thus, swapping states between chains occurs continuously. In the current work, we extend this idea to the context of time-discrete Markov chains and present two Markov chain Monte Carlo algorithms that follow the same paradigm as the continuous-time infinite swapping procedure. We analyze the convergence properties of such discrete-time algorithms in terms of their spectral gap, and implement them to sample from different target distributions. Numerical results show that the proposed methods significantly improve over more traditional sampling algorithms such as Random Walk Metropolis and (traditional) Parallel Tempering.

Published in statistics and computing https://link.springer.com/article/10.1007/s11222-021-10042-6

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Cited by in corpus (4)

• Analysis of Stochastic Gradient Descent in Continuous Time
• Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem
• Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
• Sampling Methods for Bayesian Inference Involving Convergent Noisy Approximations of Forward Maps