Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables (1996)

by David Maxwell Chickering , David Heckerman
Venue:Machine Learning
Citations:155 - 9 self

Active Bibliography

710 A tutorial on learning with Bayesian networks – David Heckerman - 1995
64 A Bayesian Approach to Causal Discovery – David Heckerman, Christopher Meek, Gregory Cooper - 1997
393 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
27 Learning Probabilistic Networks – Paul J Krause - 1998
155 Probabilistic independence networks for hidden Markov probability models – Padhraic Smyth, David Heckerman, Michael I. Jordan - 1997
102 Machine-Learning Research -- Four Current Directions – Thomas G. Dietterich
A guide to the literature on learning probabilistic . . . – Wray Buntine
156 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
6 Computationally efficient methods for selecting among mixtures of graphical models – B. Thiesson, C. Meek, D. M. Chickering, D. Heckerman - 1999
24 Learning mixtures of DAG models – Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman - 1997
214 Operations for Learning with Graphical Models – Wray L. Buntine - 1994
8 Learning Bayes net structure from sparse data sets – Kevin P. Murphy - 2001
7 Population Markov Chain Monte Carlo – Kathryn Blackmond Laskey, James Myers - 2003
27 Classification and Regression using Mixtures of Experts – Steven Richard Waterhouse - 1997
16 Learning with Mixtures of Trees – Marina Meila-Predoviciu - 1999
4 Technical Introduction: A Primer on Probabilistic Inference – Thomas L. Griffiths, Alan Yuille - 2006
16 Learning Mixtures of Bayesian Networks – Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman - 1997
Just Enough Die-Level Test: Optimizing IC Test via Machine Learning and Decision Theory – Tony Fountain - 1998
MLnet Summer School on Machine Learning and Knowledge Acquisition: LEARNING AND PROBABILITIES – Wray Buntine