Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit

TL;DR

This paper investigates the parallelization of multiproposal MCMC algorithms, especially in the large proposal limit, revealing new methods, relationships, and insights into their performance and tuning.

Contribution

It introduces new algorithms and theoretical insights into MP-MCMC in the large proposal limit, enhancing understanding of parallelism and efficiency.

Findings

Identifies promising new MP-MCMC algorithms.

Renders certain existing approaches obsolete.

Discovers relationships between different MP-MCMC methods.

Abstract

Multiproposal MCMC (MP-MCMC) algorithms use clouds of proposals to efficiently traverse state spaces and overcome complex target geometries. While MCMC methods are embarrassingly parallel by nature, the non-trivial forms of parallelism provided by the MP-MCMC formalism sometimes leads to significant improvements over a naive approach. Here, one important tuning parameter is the number of proposals p used by a single MP-MCMC iteration. While a number of computational strategies have been proposed to efficiently leverage large numbers of proposals within the MP-MCMC paradigm, much remains unknown about these algorithms, particularly in the large p-regime. In this contribution, we discover surprising results by identifying and studying several promising new methods (Algorithm 1.1, Algorithm 3.3, Algorithm 3.4), ruling out other extant approaches and discovering new relationships between different MP-MCMC methodologies. Our analysis is centered on a general state space multiproposal involutive theory recently constructed by the authors combined with the consideration of the large p-limit kernels for MP-MCMC algorithms within a variety of different classes of proposal and acceptance structures.