Hierarchical Gaussian process mixtures for regression

Hierarchical Gaussian Process Mixtures for

Using gaussian mixtures with unknownnumber of components for mixed

Hierarchical mixed models are used to account for dependence between correlated data, in particular dependence created by a group structure within the sample. In such models, the correlation between observations is modelled by including, in the regression model, group-indexed parameters regarded as random variables, so called random eeects. Gaussian distributions are commonly used for the rando...

An Adaptive Hierarchical Method Based on Wavelet and Adaptive Filtering for MRI Denoising

MRI is one of the most powerful techniques to study the internal structure of the body. MRI image quality is affected by various noises. Noises in MRI are usually thermal and mainly due to the motion of charged particles in the coil. Noise in MRI images also cause a limitation in the study of visual images as well as computer analysis of the images. In this paper, first, it is proved that proba...

Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data

We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP faci...

Unsupervised Classification of Functions using Dirichlet Process Mixtures of Gaussian Processes

This technical report presents a novel algorithm for unsupervised clustering of functions. It proceeds by developing the theory of unsupervised classification in mixtures from the familiar mixture of Gaussian distributions, to the infinite mixture of Gaussian processes. At each stage a both a theoretical and an algorithmic exposition are presented. We consider unsupervised classification (or cl...