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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

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