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Systematic approaches to generate reversiblizations of Markov chains

7 March 2023
Michael C. H. Choi
Geoffrey Wolfer
ArXiv (abs)PDFHTML
Abstract

Given a target distribution π\piπ and an arbitrary Markov infinitesimal generator LLL on a finite state space X\mathcal{X}X, we develop three structured and inter-related approaches to generate new reversiblizations from LLL. The first approach hinges on a geometric perspective, in which we view reversiblizations as projections onto the space of π\piπ-reversible generators under suitable information divergences such as fff-divergences. With different choices of functions fff, we not only recover nearly all established reversiblizations but also unravel and generate new reversiblizations. Along the way, we unveil interesting geometric results such as bisection properties, Pythagorean identities, parallelogram laws and a Markov chain counterpart of the arithmetic-geometric-harmonic mean inequality governing these reversiblizations. This further serves as motivation for introducing the notion of information centroids of a sequence of Markov chains and to give conditions for their existence and uniqueness. Building upon the first approach, we view reversiblizations as generalized means. In this second approach, we construct new reversiblizations via different natural notions of generalized means such as the Cauchy mean or the dual mean. In the third approach, we combine the recently introduced locally-balanced Markov processes framework and the notion of convex ∗*∗-conjugate in the study of fff-divergence. The latter offers a rich source of balancing functions to generate new reversiblizations.

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