Nonlinear regression using smooth Bayesian estimation

Abderrahim Halimi; Corinne Mailhes; Jean Yves Tourneret

doi:10.1109/ICASSP.2015.7178448

Nonlinear regression using smooth Bayesian estimation

Abderrahim Halimi, Corinne Mailhes, Jean Yves Tourneret

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.

Original language	English
Title of host publication	2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
Publisher	IEEE
Pages	2634-2638
Number of pages	5
ISBN (Electronic)	9781467369978
DOIs	https://doi.org/10.1109/ICASSP.2015.7178448
Publication status	Published - 4 Aug 2015
Event	40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015

Conference

Conference	40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated title	ICASSP 2015
Country/Territory	Australia
City	Brisbane
Period	19/04/15 → 24/04/15

Keywords

Bayesian algorithm
Hamiltonian Monte-Carlo
MCMC
Parameter estimation
Radar altimetry

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.2015.7178448

Cite this

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title = "Nonlinear regression using smooth Bayesian estimation",

abstract = "This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.",

keywords = "Bayesian algorithm, Hamiltonian Monte-Carlo, MCMC, Parameter estimation, Radar altimetry",

author = "Abderrahim Halimi and Corinne Mailhes and Tourneret, {Jean Yves}",

year = "2015",

month = aug,

day = "4",

doi = "10.1109/ICASSP.2015.7178448",

language = "English",

pages = "2634--2638",

booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",

publisher = "IEEE",

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note = "40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015, ICASSP 2015 ; Conference date: 19-04-2015 Through 24-04-2015",

}

Halimi, A, Mailhes, C & Tourneret, JY 2015, Nonlinear regression using smooth Bayesian estimation. in 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings. IEEE, pp. 2634-2638, 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015, Brisbane, Australia, 19/04/15. https://doi.org/10.1109/ICASSP.2015.7178448

TY - GEN

T1 - Nonlinear regression using smooth Bayesian estimation

AU - Halimi, Abderrahim

AU - Mailhes, Corinne

AU - Tourneret, Jean Yves

PY - 2015/8/4

Y1 - 2015/8/4

N2 - This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.

AB - This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.

KW - Bayesian algorithm

KW - Hamiltonian Monte-Carlo

KW - MCMC

KW - Parameter estimation

KW - Radar altimetry

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U2 - 10.1109/ICASSP.2015.7178448

DO - 10.1109/ICASSP.2015.7178448

M3 - Conference contribution

AN - SCOPUS:84946076389

SP - 2634

EP - 2638

BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings

PB - IEEE

T2 - 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015

Y2 - 19 April 2015 through 24 April 2015

ER -

Nonlinear regression using smooth Bayesian estimation

Abstract

Conference

Keywords

ASJC Scopus subject areas

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