Table of Contents

• Probabilistic kernel regression modelsTommi S. Jaakkola, David Haussler. [doi]
• Efficient mining of statistical dependenciesTim Oates, Matthew D. Schmill, Paul R. Cohen, Casey Durfee. [doi]
• Visual design support for probabilistic network applicationAxel Vogler. [doi]
• An experiment in causal discovery using a pneumona databasePeter Spirtes, Gregory F. Cooper. [doi]
• Hierarchical mixtures-of-experts for generalized linear models: some results on denseness and consistencyWenxin Jiang, Martin A. Tanner. [doi]
• Causal Mechanisms and Classification Trees for Predicting Chemical CarcinogensLouis Anthony Cox Jr.. [doi]
• Tractable structure search in the presence of latent variablesThomas Richardson, Heiko Bailer, Moulinath Banarjees. [doi]
• A learning rule based method of feature extraction with application to acoustic signal classificationMichael Larkin.
• Modeling decision tree performance with the power lawLewis J. Frey, Douglas H. Fisher. [doi]
• Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approachesEamonn J. Keogh, Michael J. Pazzani. [doi]
• A latent variable model for multivariate discretizationStefano Monti, Gregory F. Cooper. [doi]
• Parameter learning from incomplete data for Bayesian networksRobert G. Cowell.
• Bayesian graphical models, intention-to-treat, and the rubin causal ModelDavid Madigan. [doi]
• Hierarchical IFA Belief NetworksHagai Attias. [doi]
• Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition modelsDaniela Golinelli, David Madigan, Guido Consonni. [doi]
• Geometry, moments and Bayesian networks with hidden variablesRaffaella Settimi, Jim Q. Smith. [doi]
• Stochastic local search for Bayesian networkKalev Kask, Rina Dechter.
• Model folding for data subject to nonresponsePaola Sebastiani, Marco Ramoni. [doi]
• Structure optimization of density estimation models applied to regression problems with dynamic noiseMartin Kreutz, Anja M. Reimetz, Bernhard Sendhoff, Claus Weihs, Werner von Seelen. [doi]
• The exploration of new methods for learning in binary Boltzmann machinesKeith Humphreys, D. M. Titterington. [doi]
• Learning conditional probabilities from incomplete databases - An experimental comparisonMarco Ramoni, Paola Sebastiani. [doi]
• Conditional products: An alternative approach to conditional independenceA. Philip Dawid, Milan Studený. [doi]
• A note on the comparison of polynomial selection methodsMurlikrishna Viswanathan, Chris S. Wallace. [doi]
• Local experts combination through density decompositionAhmed Rida, Abderrahim Labbi, Christian Pellegrini. [doi]
• Mean field inference in a general probabilistic settingMichael Haft, Reimar Hofmann, Volker Tresp. [doi]
• A bayesian model for collaborative filteringYung-Hsin Chien, Edward I. George. [doi]
• Mixture model clustering with the multimix programMurray A. Jorgensen, Lynette A. Hunt. [doi]
• Exploring the robustness of Bayesian and information-theoretic methods for predictive inferencePetri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri.
• Process-oriented evaluation: The next stepPedro M. Domingos. [doi]
• Joint probabilistic clustering of multivariate and sequential dataPadhraic Smyth.
• Statistical challenges to inductive inference in linked dataDavid Jensen.
• Efficient learning using constrained sufficient statisticsNir Friedman, Lise Getoor. [doi]
• On the geometry of DAG models with hidden variablesDan Geiger, David Heckerman, Henry King, Christopher Meek. [doi]
• Analysis of multivariate time series via a hidden graphical modelElena Stanghellini, Joe Whittaker. [doi]
• Efficient optimization of large k real-time control algorithmPeter J. Schubert, Daniel H. Loughlin. [doi]
• Pattern discovery via entropy minimizationMatthew Brand. [doi]
• Learned models for continuous planningMatthew D. Schmill, Tim Oates, Paul R. Cohen. [doi]
• Geometric modeling of a nuclear environmentJan De Geeter, Marc Decréton, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel. [doi]
• On the application of the bootstrap for computing confidence measures on features of induced Bayesian networksNir Friedman, Moisés Goldszmidt, Abraham J. Wyner. [doi]
• Boosting methodology for regression problemsGreg Ridgeway, David Madigan, Thomas Richardson. [doi]
• Transfer of information between system and evidence modelsRussell G. Almond, Edward Herskovits, Robert J. Mislevy, Linda Stienberg. [doi]
• Entropy-driven inference and inconsistencyWilhelm Rödder, Longgui Xu. [doi]
• Testing regression models with fewer regressorsJudea Pearl, Peyman Meshkat. [doi]
• Learning extensible multi-entity directed graphical modelsKathryn Blackmond Laskey. [doi]
• Model choice: A minimum posterior predictive loss approachSujit Kumar Ghosh, Alan E. Gelfand. [doi]