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Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection

Hutter, Marcus; Zaffalon, Marco


Given the joint chances of a pair of random variables one can compute quantities of interest, like the mutual information. The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances. A common treatment of incomplete data is to assume ignorability and determine the chances by the expectation maximization (EM) algorithm. The two different methods above are well established but typically...[Show more]

CollectionsANU Research Publications
Date published: 2003
Type: Conference paper
Source: Proceedings of the 26th German Conference on Artificial Intelligence (KI-2003)
DOI: 10.1007/b13477


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