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AIC Seminar Series

Lifted Inference for Relational Continuous Models

Jaesik ChoiUniversity of Illinois at Urbana-Champaign[Home Page]

Notice:  Hosted by Rodrigo Braz

Date:  2010-06-24 at 16:00

Location:  EJ228 (SRI E building). slides via WebEx from 3:45pm on, sound at 1-888-355-1249, 749045  (Directions)

   Abstract

Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper presents a new exact lifted inference algorithm for RCMs, so that it scales up to large models of real world applications. The algorithm applies to relational pairwise models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it is an efficient lifted inference algorithm. When a relational model has Gaussian potentials, it takes only linear-time compared with previous methods which take O(|U|3) (U is a set of ground variables). Second, it is the first exact inference algorithm which handles RCMs in a lifted way. The algorithm is illustrated over an example from Econometrics. Experimental results show that our algorithm outperforms both a ground-level inference algorithm and an algorithm built with previously-known lifted methods.

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