On equal-input and monotone Markov matrices
Probability
2022-09-27 v3
Abstract
The practically important classes of equal-input and of monotone Markov matrices are revisited, with special focus on embeddability, infinite divisibility, and mutual relations. Several uniqueness results for the classic Markov embedding problem are obtained in the process. To achieve our results, we need to employ various algebraic and geometric tools, including commutativity, permutation invariance and convexity. Of particular relevance in several demarcation results are Markov matrices that are idempotents.
Cite
@article{arxiv.2007.11433,
title = {On equal-input and monotone Markov matrices},
author = {Michael Baake and Jeremy Sumner},
journal= {arXiv preprint arXiv:2007.11433},
year = {2022}
}
Comments
33 pages; final version with various small adjustments