GPstruct: Bayesian Structured Prediction Using Gaussian Processes

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (MN), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inferen...

Bayesian Gaussian Processes for Sequential Prediction, Optimisation and Quadrature

Bayesian Warped Gaussian Processes

Warped Gaussian processes (WGP) [1] model output observations in regression tasks as a parametric nonlinear transformation of a Gaussian process (GP). The use of this nonlinear transformation, which is included as part of the probabilistic model, was shown to enhance performance by providing a better prior model on several data sets. In order to learn its parameters, maximum likelihood was used...

Gaussian Processes for Prediction

We propose a powerful prediction algorithm built upon Gaussian processes (GPs). They are particularly useful for their flexibility, facilitating accurate prediction even in the absence of strong physical models. GPs further allow us to work within a complete Bayesian probabilistic framework. As such, we show how the hyperparameters of our system can be marginalised by use of Bayesian Monte Carl...

Bayesian Classiication with Gaussian Processes

reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Abstract We consider the problem of assigning an input vector x to one of m classes by predicting P(cjx) for c = 1; : : : ; m. For a two-cla...