
unknown title
– Learning Bayesian

849

A tutorial on learning with Bayesian networks
– David Heckerman
 1995

178

Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
– David Maxwell Chickering, David Heckerman
 1997

36

Learning Probabilistic Networks
– Paul J Krause
 1998

564

Dynamic Bayesian Networks: Representation, Inference and Learning
– Kevin Patrick Murphy
 2002

172

A Guide to the Literature on Learning Probabilistic Networks From Data
– Wray Buntine
 1996

167

Probabilistic independence networks for hidden Markov probability models
– Padhraic Smyth, David Heckerman, Michael I. Jordan
 1996

114

MachineLearning Research  Four Current Directions
– Thomas G. Dietterich


Aspects of the Interface between STatistics and . . .
– Matt Whiley
 1999

12

Learning Bayes net structure from sparse data sets
– Kevin P. Murphy
 2001


CONTENTS Causal Networks Learning Acausal Networks Learning Influence Diagrams Learning CausalNetwork Parameters Learning CausalNetwork Structure
– David Heckerman

58

A Bayesian approach to learning causal networks
– David Heckerman
 1995

2

Bayesian Networks for Genomic Analysis
– Paola Sebastiani, Maria M. Abad, Marco F. Ramoni
 2004

3

Towards an inclusion driven learning of Bayesian Networks
– Robert Castelo, Tomas Kocka
 2002

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


MLnet Summer School on Machine Learning and Knowledge Acquisition: LEARNING AND PROBABILITIES
– Wray Buntine

3

On the Accuracy of Stochastic Complexity Approximations
– Petri Kontkanen, Petri Myllymaki, Tomi Sil, Henry Tirri

65

Causal independence for probability assessment and inference using Bayesian networks
– David Heckerman, John S. Breese
 1994
