# Convex Combination Belief Propagation Algorithms

arXiv: https://arxiv.org/abs/2105.12815 · HTML: https://mlsanity.com/paper/2105.12815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 3 of 3)

- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Self-Guided Belief Propagation -- A Homotopy Continuation Method](https://mlsanity.com/paper/1812.01339/) (2018)
- [Diffusion Methods for Classification with Pairwise Relationships](https://mlsanity.com/paper/1505.06072/) (2015)

## Built on by (most cited first, 1 of 1)

- [Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach](https://mlsanity.com/paper/2310.19138/) (2023)

## Abstract (arXiv)

We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief propagation and other heuristics fail to converge. Belief propagation is guaranteed to converge when the underlying graphical model is acyclic, but can fail to converge and is sensitive to initialization when the underlying graph has complex topology. This paper describes modifications to the standard belief propagation algorithms that lead to methods that converge to unique solutions on graphical models with arbitrary topology and potential functions.