NDLI: Extending maximum entropy techniques to entropy constraints

Content Provider	IEEE Xplore Digital Library
Author	Gang Xiang Kreinovich, V.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, University of Texas at El Paso, El Paso, Texas 79968 (Kreinovich, V.) \|\| Philips Healthcare, El Paso, Texas 79902 (Gang Xiang)
Abstract	In many practical situations, we have only partial information about the probabilities. In some cases, we have crisp (interval) bounds on the probabilities and/or on the related statistical characteristics. In other situations, we have fuzzy bounds, i.e., different interval bounds with different degrees of certainty. In a situation with uncertainty, we do not know the exact value of the desired characteristic. In such situations, it is desirable to find its worst possible value, its best possible value, and its “typical” value – corresponding to the “most probable” probability distribution. Usually, as such a “typical” distribution, we select the one with the largest value of the entropy. This works perfectly well in usual cases when the information about the distribution consists of the values of moments and other characteristics. For example, if we only know the first and the second moments, then the distribution with the largest entropy if the normal (Gaussian) one. However, in some situations, we know the entropy (= amount of information) of the distribution. In this case, the maximum entropy approach does not work, since all the distributions which are consistent with our knowledge have the exact sam e entropy value. In this paper, we show how the main ideas of the maximum entropy approach can be extended to this case.
Starting Page	1
Ending Page	7
File Size	532341
Page Count	7
File Format	PDF
ISBN	9781424478590
e-ISBN	9781424478583
e-ISBN	9781424478576
DOI	10.1109/NAFIPS.2010.5548264
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-12
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Probability distribution Uncertainty Medical services Computer science Economic forecasting Probability density function
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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