Heat kernel estimates for subordinate Markov processes and their applications
Probability
2022-01-28 v2 Analysis of PDEs
Abstract
In this paper, we establish sharp two-sided estimates for transition densities of a large class of subordinate Markov processes. As applications, we show that the parabolic Harnack inequality and H\"older regularity hold for parabolic functions of such processes, and derive sharp two-sided Green function estimates.
Keywords
Cite
@article{arxiv.2103.10152,
title = {Heat kernel estimates for subordinate Markov processes and their applications},
author = {Soobin Cho and Panki Kim and Renming Song and Zoran Vondraček},
journal= {arXiv preprint arXiv:2103.10152},
year = {2022}
}
Comments
43 pages : Some heat kernel estimates are written in different forms