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Title: Bayesian bin distribution inference and mutual information
Authors: Endres, Dominik Maria
Foldiak, Peter
Keywords: Bayesian inference
Entropy
Model selection
Mutual information
QA Mathematics
Issue Date: Nov-2005
Citation: Endres , D M & Foldiak , P 2005 , ' Bayesian bin distribution inference and mutual information ' IEEE Transactions on Information Theory , vol 51 , no. 11 , pp. 3766-3779 .
Abstract: We present an exact Bayesian treatment of a simple, yet sufficiently general probability distribution model. We consider piecewise-constant distributions' P(X) with uniform (second-order) prior over location of discontinuity points and assigned chances. The predictive distribution and the model complexity can be determined completely from the data in a computational time that is linear in the number of degrees of freedom and quadratic in the number of possible values of X. Furthermore, exact values of the expectations of entropies and their variances can be computed with polynomial effort. The expectation of the mutual information becomes thus available, too, and a strict upper bound on its variance. The resulting algorithm is particularly useful in experimental research areas where the number of available samples is severely limited (e.g., neurophysiology). Estimates on a simulated data set provide more accurate results than using a previously proposed method.
Version: Publisher PDF
Status: Peer reviewed
URI: http://hdl.handle.net/10023/1592
DOI: http://dx.doi.org/10.1109/TIT.2005.856954
ISSN: 0018-9448
Type: Journal article
Rights: (c) 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Appears in Collections:University of St Andrews Research
Psychology & Neuroscience Research



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