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2005 JMLR JMLR 2005

Local Propagation in Conditional Gaussian Bayesian Networks

Abstract

This paper describes a scheme for local computation in conditional Gaussian Bayesian networks that combines the approach of Lauritzen and Jensen (2001) with some elements of Shachter and Kenley (1989). Message passing takes place on an elimination tree structure rather than the more compact (and usual) junction tree of cliques. This yields a local computation scheme in which all calculations involving the continuous variables are performed by manipulating univariate regressions, and hence matrix operations are avoided. [abs] [ pdf ][ bib ] © JMLR 2005. (edit, beta)

📈 Trend Setter — Bayesian Learning
🧭 Keyword Pioneer — elimination tree
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐣 Hot Topic Early Bird — probabilistic inference

Authors

Robert G. Cowell

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Graphical Models Machine Learning > Bayesian & Probabilistic > Bayesian Inference

Keywords

probabilistic inference message passing bayesian network conditional gaussian elimination tree local computation
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