Work

Anytime Anyspace AND/OR Search for Bounding the Partition Function

Year: 2017

Type: conference-paper

Abstract: Bounding the partition function is a key inference task in many graphical models. In this paper, we develop an anytime anyspace search algorithm taking advantage of AND/OR tree structure and optimized variational heuristics to tighten deterministic bounds on the partition function. We study how our priority-driven best-first search scheme can improve on state-of-the-art variational bounds in an anytime way within limited memory resources, as well as the effect of the AND/OR framework to exploit conditional independence structure within the search process within the context of summation. We compare our resulting bounds to a number of existing methods, and show that our approach offers a number of advantages on real-world problem instances taken from recent UAI competitions.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v31i1.10667

Authors: Qi Lou, Rina Dechter, Alexander Ihler

Institution: University of California, Irvine

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Defense Advanced Research Projects Agency, U.S. Air Force

Awards: RI: Small: Heuristic Search Algorithms for Probabilistic Graphical Models, G2077352727, CAREER: Estimation and Decisions in Graphical Models, G4713059963, G6724302884