### 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 | English |
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Título de la publicación alojada | Bayesian Inference and Maximum Entropy Methods in Science and Engineering |

Subtítulo de la publicación alojada | Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 |

Editorial | American Institute of Physics Inc. |

Volumen | 1853 |

ISBN (versión digital) | 9780735415270 |

DOI | |

Estado | Published - 9 jun 2017 |

Evento | 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 - Ghent, Belgium Duración: 10 jul 2016 → 15 jul 2016 |

### Conference

Conference | 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2016 |
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País | Belgium |

Ciudad | Ghent |

Período | 10/07/16 → 15/07/16 |

### Huella dactilar

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Citar esto

*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

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*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, 10/07/16. DOI: 10.1063/1.4985376

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

Resultado de la investigación: Research - revisión exhaustiva › Conference 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 -