Systematic approaches to generate reversiblizations of non-reversible Markov chains
Given a target distribution $\pi$ and an arbitrary Markov infinitesimal generator $L$ on a finite state space $\mathcal{X}$, in this talk we develop three structured and inter-related approaches to generate new reversiblizations from $L$. The first approach hinges on a geometric perspective of reversiblizations, in which we view reversiblizations as projections onto the space of $\pi$-reversible generators under suitable information divergence such as $f$-divergence. Different choices of $f$ allow us to recover almost all known reversiblizations while at the same time unravel and generate new reversiblizations. Along the way, we give 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 also motivates us to introduce 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 the second approach, and 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 $f$-divergence, which offers a rich source of balancing functions, to generate new reversiblizations.
This is based on joint work with Geoff Wolfer (RIKEN AIP).