论文标题
应用于约束满意度问题的高阶概率图形模型的增量推断
Incremental Inference on Higher-Order Probabilistic Graphical Models Applied to Constraint Satisfaction Problems
论文作者
论文摘要
概率图形模型（PGM）是解决复杂概率关系的工具。但是，次优的PGM结构主要用于实践。本文为PGM文献提供了三项贡献。第一个是图形图形上的因子图和群集图（例如Sudokus）之间的比较 - 表明偏爱群集图具有显着优势。第二个是集群图的应用到制图中的实际问题：陆地覆盖分类的提升。第三个是针对约束满意度问题的PGMS公式和一种称为“清除与合并”的算法，以解决这些问题对于传统PGM的问题太复杂。
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.
