Primer papers

Buntine (1991) Theory refinement on Bayesian networks, UAI.

Cooper & Herskovits (1992) A Bayesian method for the induction of probabilistic networks from data, Machine Learning.

Madigan & York (1995) Bayesian graphical models for discrete data, Int'l Stat Review.

Heckerman, Geiger & Chickering (1995) Learning Bayesian networks: The combination of knowledge & statistical data, Machine Learning.

Chickering, Geiger, & Heckerman (1995) Learning Bayesian networks: Search methods and experimental results. AI and Statistics.

Chickering, Heckerman, & Meek (1997) A Bayesian approach to learning Bayesian networks with local structure. UAI.

Heckerman, Meek & Cooper (1999) A Bayesian approach to causal discovery. In Computation, Causation, Discovery.

Chickering (2002) Optimal structure identification with greedy search. Journal of Machine Learning Research.

Friedman & Koller (2003) Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning.

...and beyond

Chow & Liu (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans Info Theory.

Friedman, Murphy, & Russell (1998) Learning the structure of dynamic probabilistic networks. UAI.

Ghahramani (1998) Learning dynamic Bayesian networks. In Adaptive Processing of Sequences and Data Structures.

Murphy & Mian (1999) Modelling gene expression data using dynamic Bayesian networks. Technical Note.

Friedman, Linial, Nachman, & Pe'er (2000) Using Bayesian networks to analyze expression data. Journal of Computational Biology.

Meila & Jaakkola (2000) Tractable Bayesian learning of tree belief networks. UAI.

Murphy (2001) Active learning of causal Bayes net structure. Technical Report.

Tong & Koller (2001) Active learning for structure in Bayesian Networks. IJCAI.

Beal, Falciani, Ghahramani, Rangel, & Wild (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics.

Recent additions to this list

Friedman & Goldszmidt (1996) Learning Bayesian networks with local structure. UAI .

Friedman (1997). Learning belief networks in the presence of missing values and hidden variables. ICML.

Scheines (1997) An Introduction to Causal Inference, in Causality in Crisis. University of Notre Dame Press.

Heckerman (1999) A tutorial on learning with bayesian networks, in Learning in Graphical Models. MIT Press.

Friedman, Pe'er, & Nachman (1999). Learning Bayesian Network Structure from Massive Datasets: The ``Sparse Candidate'' Algorithm. UAI .

Elidan, Lotner, Friedman & Koller (2000). Discovering hidden variables: A structure-based approach. NIPS.

MacKay (2003) Information Theory, Inference and Learning Algorithms. Cambridge University Press. (See section 35.3, p. 447---a critique of Heckerman et al's priors).

Neapolitan (2003) Learning Bayesian Networks. Prentice-Hall.

Silva, Scheines, Glymour, & Spirtes (2003). Learning measurement models for unobserved variables. UAI.

Moore & Wong (2003) Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning. ICML.

Goldenberg & Moore (2004) Tractable Learning of Large Bayes Net Structures from Sparse Data.ICML.

Interested?

mduff@uci.edu