back icon
close icon

Capture phrases in quotes for more specific queries (e.g. "rocket ship" or "Fred Lynn")

Conference Proceeding  June 19, 2020

Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories

SRI Authors Rodrigo de Salvo Braz

Citation

COPY

Rodrigo de Salvo Braz, Ciaran O’Reilly. Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories. Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017.

Abstract

Probabilistic Inference Modulo Theories (PIMT) is a recent framework that expands exact inference on graphical models to use richer languages that include arithmetic, equalities, and inequalities on both integers and real numbers. In this paper, we expand PIMT to a lifted version that also processes random functions and relations. This enhancement is achieved by adapting Inversion, a method from Lifted First-Order Probabilistic Inference literature, to also be modulo theories. This results in the first algorithm for exact probabilistic inference that efficiently and simultaneously exploits random relations and functions, arithmetic, equalities and inequalities.

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you. Expect a response within 48 hours.

Our Privacy Policy