Nonlinear regression using smooth Bayesian estimation

Abderrahim Halimi, Corinne Mailhes, Jean Yves Tourneret

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
Number of pages5
ISBN (Electronic)9781467369978
Publication statusPublished - 4 Aug 2015
Event40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015


Conference40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated titleICASSP 2015


  • 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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