Probabilistic Independence Networks for Hidden Markov Probability Models

DOI10.1162/neco.1997.9.2.227zbMath0869.68083OpenAlexW2147496287WikidataQ41444163 ScholiaQ41444163MaRDI QIDQ3125232

Michael I. Jordan, David Heckerman, Padhraic Smyth

Publication date: 18 March 1997

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://authors.library.caltech.edu/13671/

zbMATH Keywords

hidden Markov modelsgraphical techniquesprobabilistic independence networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

• On the effective implementation of the iterative proportional fitting procedure
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• Estimating the dimension of a model
• Sequential updating of conditional probabilities on directed graphical structures
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• An Introduction to Spatial Point Processes and Markov Random Fields
• Probabilistic expert systems and graphical modelling: a case study in drug safety
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Independence properties of directed markov fields
• Statistical Inference for Probabilistic Functions of Finite State Markov Chains

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