activity
20232026
most citedInformation divergences of Markov chains and their applications

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ML2026

Spectral partitioning for -block averaging kernels of finite Markov chains

Michael C. H. Choi, Youjia Wang

We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. For a partition , th…

math.PR2025

Group-averaged Markov chains II: tuning of group action in finite state space

Michael C. H. Choi, Ryan J. Y. Lim, Youjia Wang

We study group-averaged Markov chains obtained by augmenting a -stationary kernel with orbit kernels induced by a group action. We analyse the Gibbs (), Metropolis--Hasti…

math.PR2025

Group-averaged Markov chains: mixing improvement

Michael C. H. Choi, Youjia Wang

For Markov kernels on a general state space , we introduce a new class of averaged Markov kernels of induced by a group that acts on $\mathca…

math.PR2024

Improving the convergence of Markov chains via permutations and projections

Michael C. H. Choi, Max Hird, Youjia Wang

This paper aims at improving the convergence to equilibrium of finite ergodic Markov chains via permutations and projections. First, we prove that a specific mixture of permuted Ma…

math.PR2024

Information-theoretic classification of the cutoff phenomenon in Markov processes

Youjia Wang, Michael C. H. Choi

We investigate the cutoff phenomenon for Markov processes under information divergences such as -divergences and Rényi divergences. We classify most common divergences into four…

math.PR2024

Geometry and factorization of multivariate Markov chains with applications to MCMC acceleration and approximate inference

Michael C. H. Choi, Youjia Wang, Geoffrey Wolfer

This paper analyzes the factorizability and geometry of transition matrices of multivariate Markov chains. Specifically, we demonstrate that the induced chains on factors of a prod…

cs.IT2023★ 1 cited

Information divergences of Markov chains and their applications

Youjia Wang, Michael C. H. Choi

In this paper, we first introduce and define several new information divergences in the space of transition matrices of finite Markov chains which measure the discrepancy between t…