AIC Seminar Series
Lifted Inference for Relational Continuous Models
|Jaesik Choi||University 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)
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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