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Communication Dans Un Congrès Année : 2011

A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models

Résumé

This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. Within this modelling, time series are divided into segments and a linear regression analysis is performed on each segment. Unlike piecewise regression model, smooth transition functions are introduced to model smooth transitions between the sub-models. Appropriate prior distributions are associated with each parameter to penalize a data-driven criterion, leading to a fully Bayesian model. Then, a reversible jump Markov Chain Monte Carlo algorithm is derived to sample the parameter posterior distributions. It allows one to compute standard Bayesian estimators, providing a sparse representation of the data. Results are obtained for real-world electrical transients with a view to non-intrusive load monitoring applications.
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Dates et versions

hal-00609133 , version 1 (18-07-2011)

Identifiants

  • HAL Id : hal-00609133 , version 1

Citer

Matthieu Sanquer, Florent Chatelain, Mabrouka El-Guedri, Nadine Martin. A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models. ICASSP 2011 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2011, Prague, Czech Republic. pp.s/n. ⟨hal-00609133⟩
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