A method for density estimation based on expectation identities

Joaquín Peralta, Claudia Loyola, Humberto Loguercio, Sergio Davis

Resultado de la investigación: Capítulo en libro / informe / procedimiento de conferenciaConference contribution

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 originalEnglish
Título de la publicación principalBayesian Inference and Maximum Entropy Methods in Science and Engineering
Subtítulo de la publicación principalProceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016
EditorAmerican Institute of Physics Inc.
Volumen1853
ISBN (electrónico)9780735415270
Identificadores de objetos digitales
EstadoPublished - 9 jun 2017
Evento36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 - Ghent, Belgium

Conference

Conference36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016
PaísBelgium
CiudadGhent
Período10/07/1615/07/16

Huella dactilar

linear systems
logarithms
theorems
sampling
expansion
coefficients

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Citar esto

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.. DOI: 10.1063/1.4985376

Peralta, Joaquín; Loyola, Claudia; Loguercio, Humberto; Davis, Sergio / A method for density estimation based on expectation identities.

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 American Institute of Physics Inc., 2017. 110001.

Resultado de la investigación: Capítulo en libro / informe / procedimiento de conferenciaConference contribution

@inbook{8c3da547a85e40d695e8ecd00aebd38c,
title = "A method for density estimation based on expectation identities",
abstract = "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.",
author = "Joaquín Peralta and Claudia Loyola and Humberto Loguercio and Sergio Davis",
year = "2017",
month = "6",
doi = "10.1063/1.4985376",
volume = "1853",
booktitle = "Bayesian Inference and Maximum Entropy Methods in Science and Engineering",
publisher = "American Institute of Physics Inc.",

}

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., 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016, Ghent, Belgium, 10-15 julio. DOI: 10.1063/1.4985376

A method for density estimation based on expectation identities. / Peralta, Joaquín; Loyola, Claudia; Loguercio, Humberto; Davis, Sergio.

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 American Institute of Physics Inc., 2017. 110001.

Resultado de la investigación: Capítulo en libro / informe / procedimiento de conferenciaConference contribution

TY - CHAP

T1 - A method for density estimation based on expectation identities

AU - Peralta,Joaquín

AU - Loyola,Claudia

AU - Loguercio,Humberto

AU - Davis,Sergio

PY - 2017/6/9

Y1 - 2017/6/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85021442130&partnerID=8YFLogxK

U2 - 10.1063/1.4985376

DO - 10.1063/1.4985376

M3 - Conference contribution

VL - 1853

BT - Bayesian Inference and Maximum Entropy Methods in Science and Engineering

PB - American Institute of Physics Inc.

ER -

Peralta J, Loyola C, Loguercio H, Davis S. 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. American Institute of Physics Inc.2017. 110001. Disponible desde, DOI: 10.1063/1.4985376