A Bayesian approach to learning causal networks (1995)

by David Heckerman
Venue:In Uncertainty in AI: Proceedings of the Eleventh Conference
Citations:57 - 11 self

Active Bibliography

CONTENTS Causal Networks Learning Acausal Networks Learning Influence Diagrams Learning Causal-Network Parameters Learning Causal-Network Structure – David Heckerman
36 Learning Probabilistic Networks – Paul J Krause - 1998
299 A Tutorial on Learning Bayesian Networks – David Heckerman - 1995
913 Learning Bayesian networks: The combination of knowledge and statistical data – David Heckerman, David M. Chickering - 1995
unknown title – Learning Bayesian
65 Causal independence for probability assessment and inference using Bayesian networks – David Heckerman, John S. Breese - 1994
849 A tutorial on learning with Bayesian networks – David Heckerman - 1995
23 Likelihoods and Parameter Priors for Bayesian Networks – David Heckerman, Dan Geiger - 1995
172 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
19 Learning Causal Networks from Data: A survey and a new algorithm for recovering possibilistic causal networks – Ramon Sangüesa, Ulises Cortés - 1997
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
274 Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains – David Beckerman, Dan Geiger
130 Learning Bayesian Networks is NP-Hard – David Chickering, Dan Geiger, David Heckerman - 1994
26 Parameter priors for directed acyclic graphical models and the characterization of several probability distributions – Dan Geiger, David Heckerman - 1999
178 Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables – David Maxwell Chickering, David Heckerman - 1997
79 A Bayesian Approach to Causal Discovery – David Heckerman, Christopher Meek, Gregory Cooper - 1997
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
21 Graphical Models for Probabilistic and Causal reasoning – Judea Pearl - 2004