Sequential Bayesian kernel modelling with non-Gaussian noise.
Neural Netw
; 21(1): 36-47, 2008 Jan.
Article
en En
| MEDLINE
| ID: mdl-17983727
ABSTRACT
This paper presents a sequential Bayesian approach to kernel modelling of data, which contain unusual observations and outliers. The noise is heavy tailed described as a one-dimensional mixture distribution of Gaussians. The development uses a factorised variational approximation to the posterior of all unknowns, that helps to perform tractable Bayesian inference at two levels (1) sequential estimation of the weights distribution (including its mean vector and covariance matrix); and (2) recursive updating of the noise distribution and batch evaluation of the weights prior distribution. These steps are repeated, and the free parameters of the non-Gaussian error distribution are adapted at the end of each cycle. The reported results show that this is a robust approach that can outperform standard methods in regression and time-series forecasting.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Teorema de Bayes
/
Redes Neurales de la Computación
/
Ruido
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Neural Netw
Asunto de la revista:
NEUROLOGIA
Año:
2008
Tipo del documento:
Article
País de afiliación:
Reino Unido