nips nips2000 nips2000-41 nips2000-41-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
Abstract: A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this paper, we address the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models. A very natural approach is to search for
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