Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection
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Hutter, Marcus; Zaffalon, Marco
Description
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]
Collections | ANU Research Publications |
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Date published: | 2003 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/30828 |
Source: | Proceedings of the 26th German Conference on Artificial Intelligence (KI-2003) |
DOI: | 10.1007/b13477 |
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