Abstract:Reversibility is a key property of Markov chains, central to algorithms such as Metropolis-Hastings and other MCMC methods. Yet many applications yield non-reversible chains, motivating the problem of approximating them by reversible ones with minimal modification. We formulate this task as a matrix nearness problem and focus on the practically relevant case of sparse transition matrices. The resulting optimization problem is a quadratic programming problem, and numerical experiments illustrate the effectiveness of the approach. This framework provides a principled way to enforce reversibility and sparsity patterns in Markov chains with applications in MCMC, computational chemistry, and data-driven modeling.
From: Fabio Durastante Dr. [view email]
[v1]
Thu, 26 Feb 2026 14:44:54 UTC (400 KB)
[v2]
Tue, 23 Jun 2026 09:21:53 UTC (399 KB)
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