Chain Graphs for Learning

Abstract

Chain graphs combine directed and undirected graphs and their underlying mathematics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, clustering with conditional interaction between variables, and forms of Bayes classifiers. Chain graphs are then extended using the notation of plates so that samples and data analysis problems can be represented in a graphical model as well. Implications for learning are discussed in the conclusion.

Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)

Cited by in corpus (6)

• An Alternative Markov Property for Chain Graphs
• Cumulative distribution networks and the derivative-sum-product algorithm
• A Graph-Theoretic Analysis of Information Value
• On Separation Criterion and Recovery Algorithm for Chain Graphs
• Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar
• IPF for Discrete Chain Factor Graphs