Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection
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]
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|Source:||Proceedings of the 26th German Conference on Artificial Intelligence (KI-2003)|
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