A method for density estimation based on expectation identities

Joaquín Peralta, Claudia Loyola, Humberto Loguercio, Sergio Davis

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

We present a simple and direct method for non-parametric estimation of a one-dimensional probability density, based on the application of the recent conjugate variables theorem. The method expands the logarithm of the probability density ln P(x|I) in terms of a complete basis and numerically solves for the coefficients of the expansion using a linear system of equations. No Monte Carlo sampling is needed. We present preliminary results that show the practical usefulness of the method for modeling statistical data.

Idioma originalInglés
Título de la publicación alojadaBayesian Inference and Maximum Entropy Methods in Science and Engineering
Subtítulo de la publicación alojadaProceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016
EditorialAmerican Institute of Physics Inc.
Volumen1853
ISBN (versión digital)9780735415270
DOI
EstadoPublicada - 9 jun 2017
Evento36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 - Ghent, Bélgica
Duración: 10 jul 201615 jul 2016

Conferencia

Conferencia36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016
PaísBélgica
CiudadGhent
Período10/07/1615/07/16

Áreas temáticas de ASJC Scopus

  • Física y astronomía (todo)

Huella Profundice en los temas de investigación de 'A method for density estimation based on expectation identities'. En conjunto forman una huella única.

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    Peralta, J., Loyola, C., Loguercio, H., & Davis, S. (2017). A method for density estimation based on expectation identities. En Bayesian Inference and Maximum Entropy Methods in Science and Engineering: Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 (Vol. 1853). [110001] American Institute of Physics Inc.. https://doi.org/10.1063/1.4985376