Belief Propagation as Diffusion
Olivier Peltre
TL;DR
This paper introduces new belief propagation algorithms based on (co)homological constructions for estimating marginals in high-dimensional probability distributions.
Contribution
It presents novel belief propagation methods utilizing (co)homological structures for improved local statistical system analysis.
Findings
Algorithms effectively estimate marginals in complex systems
Utilizes topological methods for probabilistic inference
Provides a new framework for belief propagation
Abstract
We introduce novel belief propagation algorithms to estimate the marginals of a high dimensional probability distribution. They involve natural (co)homological constructions relevant for a localised description of statistical systems.
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Taxonomy
TopicsTopological and Geometric Data Analysis · Neural Networks and Applications · Bayesian Modeling and Causal Inference