Overview

The Information Society uses information and generates data. These data are stored in large and fast-growing databases and represent the challenge of exploiting these data to enhance planning, prediction, and decision making. The Bayesian Knowledge Discovery Project aims at developing methods and tools, based on sound statistical theories, to take up this challenge.

Results: The contributions can be broadly classified into two main areas

Missing Data: A deterministic learning method from incomplete databases.
Model Search: Decision theoretic foundations of Bayesian networks model selection.
RBE: A robust Bayesian estimator for incomplete databases.
Bayesian Clustering by Dynamics: A Bayesian method for clustering Markov processes.

Software: The results of the project are implemented in:

Bayesian Knowledge Discoverer (BKD): A program to learn Bayesian Networks from incomplete databases.
Robust Bayesian Classifier (RoC): A program for Supervised Bayesian Classification from incomplete databases.

People: The Bayesian Knowledge Discovery Project is main responsibility of

Marco Ramoni (Knowledge Media Institute)
Paola Sebastiani (Department of Statistics)

Publications: The results of the project are described in some research papers.

Contact: How to reach the Bayesian Knowledge Discovery Project.