Skip navigation
Skip navigation

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


File Description SizeFormat Image
01_Hutter_Bayesian_Treatment_of_2003.pdf698.86 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator