Distributed estimation and joint probabilities estimation by entropy model

Details Distributed estimation and joint probabilities estimation by entropy model Distributed estimation and joint probabilities estimation by entropy model

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..292f&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Computer Science

Information Theory

Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory

Scientific paper

This paper proposes the use of Entropy Model for distributed estimation system. Entropy Model is an entropic technique based on the minimization of conditional entropy and developed for Multi-Source/Sensor Information Fusion (MSIF) problem. We address the problem of distributed estimation from independent observations involving multiple sources, i.e., the problem of estimating or selecting one of several identity declaration, or hypothesis concerning an observed object. Two problems are considered in Entropy Model. In order to fuse observations using Entropy Model, it is necessary to know or estimate the conditional probabilities and by equivalent the joint probabilities. A common practice for estimating probability distributions from data when nothing is known (without a priori knowledge), one should prefer distributions that are as uniform as possible, that is, have maximal entropy. Next, the problem of combining (or ``fusing'') observations relating to identity hypotheses and selecting the most appropriate hypothesis about the object's identity is addressed. Much future work remains, but the results indicate that Entropy Model is a promising technique for distributed estimation. .

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