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

by David Maxwell Chickering , David Heckerman
Venue:Machine Learning
Citations:176 - 10 self

Active Bibliography

849 A tutorial on learning with Bayesian networks – David Heckerman - 1995
unknown title – Learning Bayesian
79 A Bayesian Approach to Causal Discovery – David Heckerman, Christopher Meek, Gregory Cooper - 1997
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
Aspects of the Interface between STatistics and . . . – Matt Whiley - 1999
36 Learning Probabilistic Networks – Paul J Krause - 1998
167 Probabilistic independence networks for hidden Markov probability models – Padhraic Smyth, David Heckerman, Michael I. Jordan - 1996
249 Operations for Learning with Graphical Models – Wray L. Buntine - 1994
114 Machine-Learning Research -- Four Current Directions – Thomas G. Dietterich
2 Bayesian Networks for Genomic Analysis – Paola Sebastiani, Maria M. Abad, Marco F. Ramoni - 2004
172 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
12 Population Markov Chain Monte Carlo – Kathryn Blackmond Laskey, James Myers - 2003
9 Computationally efficient methods for selecting among mixtures of graphical models – B. Thiesson, C. Meek, D. M. Chickering, D. Heckerman - 1999
25 Learning mixtures of DAG models – Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman - 1997
17 Learning with Mixtures of Trees – Marina Meila-Predoviciu - 1999
5 Technical Introduction: A Primer on Probabilistic Inference – Thomas L. Griffiths, Alan Yuille - 2006
21 Learning Mixtures of Bayesian Networks – Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman - 1997
3 On the Accuracy of Stochastic Complexity Approximations – Petri Kontkanen, Petri Myllymaki, Tomi Sil, Henry Tirri
Bayesian Modelling in Machine Learning: A Tutorial Review – Matthias Seeger - 2009