Quantitative Convergence Rates for Stochastically Monotone Markov Chains
Probability
2024-10-01 v1
Abstract
For Markov chains and Markov processes exhibiting a form of stochastic monotonicity (larger states shift up transition probabilities in terms of stochastic dominance), stability and ergodicity results can be obtained using order-theoretic mixing conditions. We complement these results by providing quantitative bounds on deviations between distributions. We also show that well-known total variation bounds can be recovered as a special case.
Cite
@article{arxiv.2409.19874,
title = {Quantitative Convergence Rates for Stochastically Monotone Markov Chains},
author = {Takashi Kamihigashi and John Stachurski},
journal= {arXiv preprint arXiv:2409.19874},
year = {2024}
}