Gaussian Processes for regression: a tutorial

Prediction with Gaussian Processes : from Linear Regression to Linear Prediction

The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with...

Prediction with Gaussian Processes from Linear Regression to Linear Prediction and Beyond

The main aim of this paper is to provide a tutorial on regression with Gaussian processes We start from Bayesian linear regression and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions rather than on priors over parameters This leads in to a more general discussion of Gaussian processes in section Section deals with further ...

The Rate of Entropy for Gaussian Processes

In this paper, we show that in order to obtain the Tsallis entropy rate for stochastic processes, we can use the limit of conditional entropy, as it was done for the case of Shannon and Renyi entropy rates. Using that we can obtain Tsallis entropy rate for stationary Gaussian processes. Finally, we derive the relation between Renyi, Shannon and Tsallis entropy rates for stationary Gaussian proc...

Gaussian processes A hands-on tutorial

Boosted Regression (Boosting): An introductory tutorial and a Stata plugin

Boosting, or boosted regression, is a recent data mining technique that has shown considerable success in predictive accuracy. This article gives an overview over boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie et al. (2001, p. 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regress...