Probabilistic independence networks for hidden Markov probability models (1996)

by Padhraic Smyth , David Heckerman , Michael I. Jordan
Citations:167 - 12 self

Documents Related by Co-Citation

7069 Probabilistic Reasoning in Intelligent Systems – J Pearl - 1988
488 The Infinite Hidden Markov Model – Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - 2002
8058 Maximum likelihood from incomplete data via the EM algorithm – A. P. Dempster, N. M. Laird, D. B. Rubin - 1977
4251 A tutorial on hidden markov models and selected applications in speech recognition – Lawrence R. Rabiner - 1989
3721 Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images – S Geman, D Geman - 1984
151 Stochastic simulation algorithms for dynamic probabilistic networks – Keiji Kanazawa, Daphne Koller, Stuart Russell - 1995
723 Hierarchical mixtures of experts and the EM algorithm – Michael I. Jordan - 1994
146 ┬¬Bayesian Updating in Recursive Graphical Models by Local Computations,┬║ Computational Statistical Quarterly – F V Jensen, S L Lauritzen, K G Olesen - 1990
1295 Local computations with probabilities on graphical structures and their application to expert systems – S L Lauritzen, D J Spiegelhalter - 1988
900 An Introduction to Bayesian Networks – F V Jensen - 1996
1124 Graphical Models – S L Lauritzen - 1996
830 An introduction to hidden Markov models – L. R. Rabiner, B. H. Juang - 1986
216 The EM algorithm for graphical association models with missing data – S L Lauritzen - 1995
44 Hidden Markov decision trees – Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul - 1997
99 Exploiting Tractable Substructures in Intractable Networks – Lawrence Saul, Michael I. Jordan - 1995
8569 Elements of Information Theory – T M Cover, J A Thomas - 1991
768 A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains – L E Baum, T Petrie, G Soules, N Weiss - 1970
766 A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants – Radford Neal, Geoffrey E. Hinton - 1998
849 A tutorial on learning with Bayesian networks – David Heckerman - 1995