title: Sequential Bayesian kernel modelling with non-Gaussian noise.
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.
authors.affiliation: Department of Computing, Goldsmiths College, University of London, London SE14 6NW, United Kingdom. N.Nikolaev@gold.ac.uk
authors.forename: Nikolay Y
authors.initials: NY
authors.lastname: Nikolaev
authors.forename: Lilian M
authors.initials: LM
authors.lastname: de Menezes
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016571:Neural Networks, Computer; D009622:Noise; D016011:Normal Distribution; D012044:Regression Analysis; D013997:Time Factors