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A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Sanquer, Matthieu Chatelain, Florent El-Guedri, Mabrouka Martin, Nadine |
| Copyright Year | 2011 |
| Abstract | 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. |
| Related Links | https://hal.science/hal-00609133/file/sanquerm.pdf |
| Conference Proceedings | ICASSP 2011 - IEEE International Conference on Acoustics, Speech and Signal Processing |
| Language | English |
| Publisher | HAL CCSD |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Conference Proceedings |
| Subject | Physics and Astronomy Computer Science |
