Colored Markov Random Fields for Probabilistic Topological Modeling

TL;DR

This paper introduces Colored Markov Random Fields (CMRFs), a novel probabilistic model that captures both conditional and marginal dependencies on topological spaces, enhancing the analysis of complex systems.

Contribution

The paper proposes CMRFs, extending Gaussian Markov Random Fields with link coloring to encode different types of independence, grounded in Hodge theory.

Findings

CMRFs outperform baselines in distributed estimation tasks.

Incorporating topological priors improves model accuracy.

Theoretical foundation links CMRFs to topological signal processing.

Abstract

Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological signal processing highlight the importance of variables defined on topological spaces in several application domains. In such cases, the underlying topology shapes statistical relationships, limiting the expressiveness of canonical PGMs. To overcome this limitation, we introduce Colored Markov Random Fields (CMRFs), which model both conditional and marginal dependencies among Gaussian edge variables on topological spaces, with a theoretical foundation in Hodge theory. CMRFs extend classical Gaussian Markov Random Fields by including link coloring: connectivity encodes conditional independence, while color encodes marginal independence. We quantify the benefits of CMRFs through a distributed estimation case study over a physical network, comparing it with baselines with different levels of topological prior.