Fast Bayes and the dynamic junction forest

DOI10.1016/S0004-3702(98)00103-9zbMath0910.68206OpenAlexW2006889840MaRDI QIDQ1277771

K. Nadia Papamichail, James Q. Smith

Publication date: 2 March 1999

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0004-3702(98)00103-9

zbMATH Keywords

influence diagramsdynamic modelsHellinger metricjunction treesmultivariate state space modelsprobabilistic expert systems

Mathematics Subject Classification ID

Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (2)

SOME NEW PROPERTIES OF HELLINGER DISTANCE FOR VALIDATING APPROXIMATIONS IN BAYESIAN ANALYSIS ⋮ Bayesian decision support for complex systems with many distributed experts

Cites Work

• Influence diagrams for statistical modelling
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Handling multiple sources of variation using influence diagrams
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs
• Sequential updating of conditional probabilities on directed graphical structures
• Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models
• Independence properties of directed markov fields
• An algebra of bayesian belief universes for knowledge‐based systems
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