Fast compression of MCMC output

TL;DR

This paper introduces cube thinning, a fast and efficient method for compressing MCMC outputs using control variates, with linear computational complexity and competitive statistical accuracy.

Contribution

It presents a novel resampling method called cube thinning that reduces MCMC output size efficiently while maintaining accuracy, leveraging control variates and the cube method.

Findings

Cube thinning has linear CPU complexity in sample size N.

It is computationally faster than Stein thinning, especially for large N.

Numerical experiments show competitive statistical error compared to existing methods.

Abstract

We propose cube thinning, a novel method for compressing the output of a MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It amounts to resampling the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on averages of these control variates, using the cube method of \cite{Deville2004}. Its main advantage is that its CPU cost is linear in $N$, the original sample size, and is constant in $M$, the required size for the compressed sample. This compares favourably to Stein thinning \citep{Riabiz2020}, which has complexity $\mathcal{O}(NM^2)$, and which requires the availability of the gradient of the target log-density (which automatically implies the availability of control variates). Our numerical experiments suggest that cube thinning is also competitive in terms of statistical error.