Constrained Monte Carlo Markov Chains on Graphs
Probability
2019-07-02 v1 Statistics Theory
Statistics Theory
Abstract
This paper presents a novel theoretical Monte Carlo Markov chain procedure in the framework of graphs. It specifically deals with the construction of a Markov chain whose empirical distribution converges to a given reference one. The Markov chain is constrained over an underlying graph, so that states are viewed as vertices and the transition between two states can have positive probability only in presence of an edge connecting them. The analysis is carried out on the basis of the relationship between the support of the target distribution and the connectedness of the graph.
Cite
@article{arxiv.1907.00779,
title = {Constrained Monte Carlo Markov Chains on Graphs},
author = {Roy Cerqueti and Emilio De Santis},
journal= {arXiv preprint arXiv:1907.00779},
year = {2019}
}
Comments
15 pages, no figures. arXiv admin note: substantial text overlap with arXiv:1803.01738