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Conference Papers Year : 2015

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.
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Dates and versions

hal-01485021 , version 1 (08-03-2017)

Identifiers

  • HAL Id : hal-01485021 , version 1
  • OATAO : 17108

Cite

Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret. Nonlinear regression using smooth Bayesian estimation. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), Apr 2015, South Brisbane, QLD, Australia. pp. 2634-2638. ⟨hal-01485021⟩
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