Inference-less density estimation using Copula Bayesian Networks

Gal Elidan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We consider learning continuous probabilistic graphical models in the face of missing data. For non-Gaussian models, learning the parameters and structure of such models depends on our ability to perform efficient inference, and can be prohibitive even for relatively modest domains. Recently, we introduced the Copula Bayesian Network (CBN) density model - a flexible framework that captures complex high-dimensional dependency structures while offering direct control over the univariate marginals, leading to improved generalization. In this work we show that the CBN model also offers significant computational advantages when training data is partially observed. Concretely, we leverage on the specialized form of the model to derive a computationally amenable learning objective that is a lower bound on the log-likelihood function. Importantly, our energy-like bound circumvents the need for costly inference of an auxiliary distribution, thus facilitating practical learning of highdimensional densities. We demonstrate the effectiveness of our approach for learning the structure and parameters of a CBN model for two reallife continuous domains.

Original languageAmerican English
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
PublisherAUAI Press
Pages151-159
Number of pages9
ISBN (Print)9780974903965
StatePublished - 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

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