Constraint Processing in Lifted Probabilistic Inference

TL;DR

This paper analyzes how constraint processing affects lifted probabilistic inference, revealing that proper methods are crucial for computational efficiency through theoretical insights and empirical validation.

Contribution

It provides the first combined theoretical and empirical analysis of constraint processing in lifted inference, highlighting its impact on complexity and performance.

Findings

Incorrect constraint processing can cause exponential complexity

Proper constraint handling improves inference efficiency

Empirical results confirm the significance of constraint processing

Abstract

First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.