Welcome!

Welcome to the home page of the Bayesian Knowledge Discovery Project, a joint effort of the Knowledge Media Institute and the Department of Statistics of The Open University.

Software: New software available on line!

RoC (Robust Bayesian Classifier) Version 1.0b for Windows 9x/NT is new!
Bayesian Knowledge Discoverer Version 1.0b for Windows 9x/NT.

Positions: Research Studentship in Knowledge Discovery at KMi.

Documentation: New documentation for users of our programs.

Bayesian Methods for Intelligent Data Analysis: A gentle introduction to Bayesian statistical methods for Knowledge Discovery and Intelligent Data Analysis.
An Introduction to the Robust Bayesian Classifier: Overview and user manual of RoC.
Frequently Asked Questions: A list of Questions and Answers for BKD Users.

Papers: New papers available on line!

M. Ramoni and P. Sebastiani, Learning Conditional Probabilities from Incomplete Data: An Experimental Comparison, in Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, Morgan Kaufman, San Mateo, CA, 1999.
P. Sebastiani and M. Ramoni, Model Folding for Data Subject to Nonresponse, in Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, Morgan Kaufman, San Mateo, CA, 1999.
M. Ramoni, P. Sebastiani, P. Cohen, J. Warwick and J. Davis, Bayesian Clustering by Dynamics, KMi Technical Report KMi-TR-78, Knowledge Media Institute, The Open University, February 1999.
P. Sebastiani, M. Ramoni, P. Cohen, J. Warwick and J. Davis, Discovering Dynamics using Bayesian Clustering, KMi Technical Report KMi-TR-77, Knowledge Media Institute, The Open University, February 1999.
P. Sebastiani, M. Ramoni and P. Cohen, Bayesian Clustering of Sensory Inputs by Dynamics, KMi Technical Report KMi-TR-76, Knowledge Media Institute, The Open University, February 1999.