Learning decomposable markov networks in pseudo-independent domains with local evaluation

Authors:
Y. Xiang;J. Lee
Affiliations:
Department of Computing and Information Science, University of Guelph, Guelph, Canada N1G 2W1;Department of Computing and Information Science, University of Guelph, Guelph, Canada N1G 2W1
Venue:
Machine Learning
Year:
2006

Citing 11
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
A “Microscopic” Study of Minimum Entropy Search in Learning Decomposable Markov Networks

Machine Learning
Probabilistic Expert Systems

Probabilistic Expert Systems
Expert Systems and Probabiistic Network Models

Expert Systems and Probabiistic Network Models
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach

Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Maximum likelihood bounded tree-width Markov networks

Artificial Intelligence
Learning Bayesian Networks

Learning Bayesian Networks
Learning belief networks in domains with recursively embedded pseudo independent submodels

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Critical remarks on single link search in learning belief networks

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

We consider learning probabilistic graphical models in a problem domain of unknown dependence structure. Common learning algorithms rely on single-link lookahead search, which assumes the underlying domain is not pseudo-independent. Since the dependence structure of the domain is unknown, such assumption is fallible. We study learning algorithms that make no such assumption and return an approximate dependence structure no matter whether the domain is pseudo-independent or not. The focus of this paper is on learning decomposable Markov networks, which can directly be used for model-based inference or as the intermediate step for further learning of directed graphical models. We identify a small subset of domain variables, termed crux, in the graphical models currently being examined during search. We prove that crux is sufficient for computing the incremental change of both model description length as well as data description length given the model. Based on crux, we propose algorithms that reduce evaluation of alternative graphical models to local computation, improve efficiency significantly, and introduce no error to the selection of alternative models.