A Note on the Comparison of Polynomial Selection Methods

Murlikrishna Viswanathan, Chris S. Wallace; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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An experiment in causal discovery using a pneumonia database

Peter Spirtes, Gregory F. Cooper; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Boosting Methodology for Regression Problems

Greg Ridgeway, David Madigan, Thomas S. Richardson; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Tractable Structure Search in the Presence of Latent Variables

Thomas Richardson, Heiko Bailer, Moulinath Banarjees; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Efficient Mining of Statistical Dependencies

Tim Oates, Matthew D. Schmill, Paul R. Cohen, Casey Durfee; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model

David Madigan; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Stochastic Local Search for Bayesian Network

Kalev Kask, Rina Dechter; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Hierarchical Mixtures-of-Experts for Generalized Linear Models: Some Results on Denseness and Consistency

Wenxin Jiang, Martin A. Tanner; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Probabilistic Kernel Regression Models

Tommi S. Jaakkola, David Haussler; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Mean field inference in a general probabilistic setting

Michael Haft, Reimar Hofmann, Volker Tresp; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Model choice: A minimum posterior predictive loss approach

Sujit Kumar Ghosh, Alan E. Gelfand; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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On the geometry of DAG models with hidden variables

Dan Geiger, David Heckerman, Henry King, Christopher Meek; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Geometric modeling of a nuclear environment

Jan De Geeter, Marc Decréton, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Efficient learning using constrained sufficient statistics

Nir Friedman, Lise Getoor; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Modeling decision tree performance with the power law

Lewis J. Frey, Douglas H. Fisher; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Process-oriented evaluation: The next step

Pedro M. Domingos; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:

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Hierarchical IFA Belief Networks

Hagai Attias; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:1-9

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Pattern Discovery via Entropy Minimization

Matthew Brand; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:10-17

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Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens

Louis Anthony Cox Jr; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:18-26

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Conditional products: An alternative approach to conditional independence

A. Philip Dawid, Milan Studený; Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:27-35

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