DeepAI

# Constrained Monte Carlo Markov Chains on Graphs

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.

• 7 publications
• 3 publications
10/07/2021

### Curved Markov Chain Monte Carlo for Network Learning

We present a geometrically enhanced Markov chain Monte Carlo sampler for...
02/01/2022

11/20/2017

### A local graph rewiring algorithm for sampling spanning trees

We introduce a Markov Chain Monte Carlo algorithm which samples from the...
06/23/2018

### On Markov chain Monte Carlo for sparse and filamentary distributions

A novel strategy that combines a given collection of reversible Markov k...
12/31/2019

### Schrödinger Bridge Samplers

Consider a reference Markov process with initial distribution π_0 and tr...
01/10/2019

### The Capacity of Count-Constrained ICI-Free Systems

A Markov chain approach is applied to determine the capacity of a genera...
11/14/2018

### Randomisation Algorithms for Large Sparse Matrices

In many domains it is necessary to generate surrogate networks, e.g., fo...