Authors: Balestrino, A.; Caiti, A.; Crisostomi, E.

Source: International Journal of Control, Volume 79, Number 9, September 2006 , pp. 1145-1155(11)

Publisher: Taylor and Francis Ltd

Abstract:

A class of algorithms for approximation of the maximum entropy estimate of probability density functions on the basis of a finite number of sampled data is introduced. The algorithms are presented as a finite sequence in order of increasing accuracy and decreasing computational efficiency in which the last element of the sequence is the exact maximum entropy estimate. Numerical and applicative examples are reported.

Document Type: Research article

DOI: http://dx.doi.org/10.1080/00207170600818373

Affiliations: 1: Departement of Electrical Systems and Automation (DSEA), University of Pisa, Via Diotisalvi 2, 56100 Pisa, Italy

Publication date: 2006-09-01

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