Differential entropy estimation by particles

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 44, Issue 1 PART 1, 2011, Pages 11991-11996

  Ajgl, Jiří ^a   Šimandl, Miroslav ^a  

^a UNIVERSITY OF WEST BOHEMIA   (Czech Republic)

Author keywords

Entropy; Estimation theory; Monte Carlo calculation; Nonlinear filters; Stochastic systems

Indexed keywords

NONLINEAR FILTERING; PROBABILITY DENSITY FUNCTION; STOCHASTIC SYSTEMS;

DIFFERENTIAL ENTROPY; ESTIMATION THEORY; MONTE CARLO CALCULATION; NONPARAMETRIC ENTROPY ESTIMATOR; SAMPLING DENSITIES; SIMULATION STUDIES; STATE DENSITIES; UNBIASED ESTIMATES;

ENTROPY;

EID: 84866761535     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20110828-6-IT-1002.01404     Document Type: Conference Paper

References (14)

1. Ajgl, J. and Šimandl, M. (2010). Survey of estimate fusion approaches. In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2010. Funchal, Portugal.
2. Beirlant, J., Dudewicz, E.J., Györfi, L., and van Der Meulen, E.C. (1997). Nonparametric entropy estimation : an overview. International Journal of the Mathematical Statistics Sciences, 6, 17-39.
3. Boers, Y., Driessen, H., Bagchi, A., and Mandal, P. (2010). Particle filter based entropy. In Proceedings of the 13th International Conference on Information Fusion. Edinburgh, UK.
4. Bolić, M., Djurić, P.M., and Hong, S. (2004). Resampling algorithms for particle filters : A computational complexity perspective. Journal of Advances in SignalProcessing, 2004(15), 1642-1648.
5. Crisan, D. and Doucet, A. (2002). A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. on Signal Processing, 50(3), 736-746.
6. Farrell, W.J. and Ganesh, C. (2009). Generalized Chernoff fusion approximation for practical distributed data fusion. In Proceedings of the 12th International Conference on Information Fusion. Seattle, Washington, USA.
7. Huber, M.F., Bailey, T., Durrant-Whyte, H., and Hanebeck, U.D. (2008). On entropy approximation for gaussian mixture random vectors. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 181-188. Seoul, Korea.
8. Leonenko, N., Pronzato, L., and Savani, V. (2008). A class of Rényi information estimators for multidimensional densities. The Annals of Statistics, 36(5), 2153-2182.
9. Miller, E.G. (2003). A new class of entropy estimators for multi-dimensional densities. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing.
10. n-nearest neighbor estimators of entropy. Mathematical Methods of Statistics, 17(3), 261-277.
11. Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, volume 26 of Monographs on Statistics and Applied Probability. Chapman and Hall, London.
12. Singh, H., Misra, N., Hnizdo, V., Fedorowicz, A., and Demchuk, E. (2003). Nearest neighbor estimates of entropy. American Journal of Mathematical and Management Sciences, 23(3&4), 301-321.
13. Skoglar, P., Orguner, U., and Gustafsson, F. (2009). On information measures based on particle mixture for optimal bearings-only tracking. In Proceedings of the IEEE Aerospace Conference. Big Sky, Montana, USA.
14. van der Merwe, R. and Wan, E. (2003). Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. In Proceedings of the Workshop on Advances in Machine Learning. Montréal, Canada.