Parallelizing MCMC sampling via space partitioning

4Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Efficient sampling of many-dimensional and multimodal density functions is a task of great interest in many research fields. We describe an algorithm that allows parallelizing inherently serial Markov chain Monte Carlo (MCMC) sampling by partitioning the space of the function parameters into multiple subspaces and sampling each of them independently. The samples of the different subspaces are then reweighted by their integral values and stitched back together. This approach allows reducing sampling wall-clock time by parallel operation. It also improves sampling of multimodal target densities and results in less correlated samples. Finally, the approach yields an estimate of the integral of the target density function.

Cite

CITATION STYLE

APA

Hafych, V., Eller, P., Schulz, O., & Caldwel, A. (2022). Parallelizing MCMC sampling via space partitioning. Statistics and Computing, 32(4). https://doi.org/10.1007/s11222-022-10116-z

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free