A multiple-try Metropolis-Hastings algorithm with tailored proposals

TL;DR

This paper introduces a novel multiple-try Metropolis-Hastings algorithm that leverages tailored proposal distributions within a graph-based framework to improve sampling efficiency.

Contribution

It proposes a new algorithm that uses a graph structure and tailored proposals to enhance Metropolis-Hastings sampling performance.

Findings

Effective in examples with predefined target and proposal distributions

Improves sampling efficiency over traditional methods

Demonstrates flexibility with tailored proposals

Abstract

We present a new multiple-try Metropolis-Hastings algorithm designed to be especially beneficial when a tailored proposal distribution is available. The algorithm is based on a given acyclic graph $G$, where one of the nodes in $G$, $k$ say, contains the current state of the Markov chain and the remaining nodes contain proposed states generated by applying the tailored proposal distribution. The Metropolis-Hastings algorithm alternates between two types of updates. The first update type is using the tailored proposal distribution to generate new states in all nodes in $G$ except in node $k$. The second update type is generating a new value for $k$, thereby changing the value of the current state. We evaluate the effectiveness of the proposed scheme in an example with previously defined target and proposal distributions.