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Research Centre
Data - Algorithms - Decision Making
Data - Algorithms - Decision Making
Established in 2005 under support of MŠMT ČR (project 1M0572)
Publications
Differential entropy estimation by particles
Typ:
Conference paper
Authors:
Proceedings name:
Proceedings of the 18th IFAC World Congress
Publisher:
Elsevier
Serie:
Milano, Italy
Year:
2011
Pages:
11991-11996
ISSN:
1474-6670
Keywords:
Stochastic systems, Estimation theory, Nonlinear filters, Mo
URL (www page):
attachment1:
clanek_ifac11_AS.pdf [file]
attachment2:
prezentace_ifac11_AS.pdf [file]
Anotation:
The paper deals with the estimation of the differential entropy of the probability density function of a stochastic system. A nonparametric entropy estimator based on particles with weights is proposed. The estimator provides asymptotically unbiased estimates. For small number of particles, the bias originates from the difference between the state density and the sampling density. A simulation study is provided. The approach to the differential entropy estimation by particles is a pillar of the fusion problem.