AISTATS 1999

20 papers

A Note on the Comparison of Polynomial Selection Methods Murlikrishna Viswanathan, Chris S. Wallace

An Experiment in Causal Discovery Using a Pneumonia Database Peter Spirtes, Gregory F. Cooper

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

Boosting Methodology for Regression Problems Greg Ridgeway, David Madigan, Thomas S. Richardson

Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens Louis Anthony Cox

Conditional Products: An Alternative Approach to Conditional Independence A. Philip Dawid, Milan Studený

Efficient Learning Using Constrained Sufficient Statistics Nir Friedman, Lise Getoor

Efficient Mining of Statistical Dependencies Tim Oates, Matthew D. Schmill, Paul R. Cohen, Casey Durfee

Geometric Modeling of a Nuclear Environment Jan De Geeter, Marc Decréton, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel

Hierarchical IFA Belief Networks Hagai Attias

Hierarchical Mixtures-of-Experts for Generalized Linear Models: Some Results on Denseness and Consistency Wenxin Jiang, Martin A. Tanner

Mean Field Inference in a General Probabilistic Setting Michael Haft, Reimar Hofmann, Volker Tresp

Model Choice: A Minimum Posterior Predictive Loss Approach Sujit Kumar Ghosh, Alan E. Gelfand

Modeling Decision Tree Performance with the Power Law Lewis J. Frey, Douglas H. Fisher

On the Geometry of DAG Models with Hidden Variables Dan Geiger, David Heckerman, Henry King, Christopher Meek

Pattern Discovery via Entropy Minimization Matthew Brand

Probabilistic Kernel Regression Models Tommi S. Jaakkola, David Haussler

Process-Oriented Evaluation: The Next Step Pedro M. Domingos

Stochastic Local Search for Bayesian Network Kalev Kask, Rina Dechter

Tractable Structure Search in the Presence of Latent Variables Thomas Richardson, Heiko Bailer, Moulinath Banarjees