From the 1 of 9 linked papers with an AI index.
9 papers
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…
On additive averaging kernels for finite Markov chains
Ryan J. Y. Lim, Michael C. H. Choi
The paper studies kernels obtained by mixing a baseline Markov transition with a Gibbs kernel, derives formulas for minimizing distance to stationarity under Frobenius norm and KL…
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…
Optimising two-block averaging kernels to speed up Markov chains
Ryan J. Y. Lim, Michael C. H. Choi
We study the problem of selecting optimal two-block partitions to accelerate the mixing of finite Markov chains under group-averaging transformations. The main objectives considere…
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 transition kernel with a group action on the state space via orbit kernels. Given a group $\mathc…
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 $\mathc…