Tech Report

Learning Conditional Probabilities from Incomplete Data: An Experimental Comparison

This paper reports some experimental results comparing three parametric methods, Gibbs Sampling, EM algorithm and Bound and Collapse, for the estimation of conditional probability distributions from incomplete databases.

1. Knowledge Media Institute, The Open University.

2. Department of Actuarial Science and Statistics, City University.

ID: kmi-98-05

Date: 1998

Author(s): Marco Ramoni and Paola Sebastiani

Download PDF

View By

Other Publications

Latest Seminar
Dr Lisa Bowers
The Open University

On the Peripheral - Design Praxis feeling the future paradigm

Watch the live webcast


Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support


If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team