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An Adaption of the Jaynes Decision Algorithm
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A Utility-Based Approach to Some Information Measures

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Entropy 2007, 9(1), 1-26; https://doi.org/10.3390/e9010001
Received: 23 May 2006 / Accepted: 5 January 2007 / Published: 20 January 2007
We review a decision theoretic, i.e., utility-based, motivation for entropy and Kullback-Leibler relative entropy, the natural generalizations that follow, and various properties of thesegeneralized quantities. We then consider these generalized quantities in an easily interpreted spe-cial case. We show that the resulting quantities, share many of the properties of entropy andrelative entropy, such as the data processing inequality and the second law of thermodynamics.We formulate an important statistical learning problem – probability estimation – in terms of ageneralized relative entropy. The solution of this problem reflects general risk preferences via theutility function; moreover, the solution is optimal in a sense of robust absolute performance. View Full-Text
Keywords: Generalized Entropy; Generalized Kullback-Leibler Relative Entropy; Decision The- ory; Expected Utility; Horse Race; Tsallis Entropy; Statistical Learning; Probability Estimation; Risk Neutral Pricing Measure Generalized Entropy; Generalized Kullback-Leibler Relative Entropy; Decision The- ory; Expected Utility; Horse Race; Tsallis Entropy; Statistical Learning; Probability Estimation; Risk Neutral Pricing Measure
MDPI and ACS Style

Friedman, C.; Huang, J.; Sandow, S. A Utility-Based Approach to Some Information Measures. Entropy 2007, 9, 1-26.

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