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

• URL: http://arxiv.org/abs/2202.12916v1
• Date: Fri, 25 Feb 2022 19:13:47 GMT
• Title: Incremental Inference on Higher-Order Probabilistic Graphical Models Applied to Constraint Satisfaction Problems
• Authors: Simon Streicher
• Abstract summary: dissertation presents three contributions to the PGM literature. First is a comparison between factor graphs and cluster graphs on graph colouring problems such as Sudokus. Second is an application of cluster graphs to a practical problem in cartography: land cover classification boosting. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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