# Incremental Inference on Higher-Order Probabilistic Graphical Models Applied to Constraint Satisfaction Problems

arXiv: https://arxiv.org/abs/2202.12916 · HTML: https://mlsanity.com/paper/2202.12916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2022.

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

- [A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery](https://mlsanity.com/paper/2206.11706/) (2022)

## Abstract (arXiv)

Probabilistic graphical models (PGMs) are tools for solving complex probabilistic relationships. However, suboptimal PGM structures are primarily used in practice. This dissertation presents three contributions to the PGM literature. The first is a comparison between factor graphs and cluster graphs on graph colouring problems such as Sudokus - indicating a significant advantage for preferring cluster graphs. The second is an application of cluster graphs to a practical problem in cartography: land cover classification boosting. The third is a PGMs formulation for constraint satisfaction problems and an algorithm called purge-and-merge to solve such problems too complex for traditional PGMs.