Non-reversible jump algorithms for Bayesian nested model selection

TL;DR

This paper introduces non-reversible jump algorithms for Bayesian nested model selection, leveraging lifting techniques to improve sampling efficiency without additional computational cost.

Contribution

It proposes a novel non-reversible version of reversible jump algorithms using lifting, enhancing Bayesian model choice sampling performance.

Findings

Non-reversible jump algorithms outperform reversible ones in empirical tests.

Lifting the model indicator variable improves mixing and convergence.

The proposed methods require no extra computational cost.

Abstract

Non-reversible Markov chain Monte Carlo methods often outperform their reversible counterparts in terms of asymptotic variance of ergodic averages and mixing properties. Lifting the state-space (Chen et al., 1999; Diaconis et al., 2000) is a generic technique for constructing such samplers. The idea is to think of the random variables we want to generate as position variables and to associate to them direction variables so as to design Markov chains which do not have the diffusive behaviour often exhibited by reversible schemes. In this paper, we explore the benefits of using such ideas in the context of Bayesian model choice for nested models, a class of models for which the model indicator variable is an ordinal random variable. By lifting this model indicator variable, we obtain non-reversible jump algorithms, a non-reversible version of the popular reversible jump algorithms introduced by Green (1995). This simple algorithmic modification provides samplers which can empirically outperform their reversible counterparts at no extra computational cost. The code to reproduce all experiments is available online.