Work

BEEM : Bucket Elimination with External Memory

Year: 2012

Type: preprint

Abstract: A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1203.3487

Authors: Kask, Kalev, Dechter, Rina, Gelfand, Andrew E.

Institutions: University of California, Irvine, Irvine University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health

Award: RI: High Performance Algorithms for Probabilistic and Deterministic Graphical Models

OpenAlex ID: W1552227334

Cites: 8

Cited by: 9

Related to: 10