Add to ORKGThe GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Scope of this work Gaussian Process models (GPMs) are extensively used in data analysis given their ...
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and pred...
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaus...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
Contains fulltext : 129937.pdf (publisher's version ) (Closed access)The GPstuff t...
2007 I, Edward Snelson, confirm that the work presented in this thesis is my own. Where information ...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
International audienceThis paper describes Gaussian process regression (GPR) models presented in pre...
Gaussian Processes (GPs) are a powerful mod-elling framework incorporating kernels and Bayesian infe...
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to ...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
In this report, we discuss the application and usage of Gaussian Process in Classification and Regre...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Scope of this work Gaussian Process models (GPMs) are extensively used in data analysis given their ...
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and pred...
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaus...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
Contains fulltext : 129937.pdf (publisher's version ) (Closed access)The GPstuff t...
2007 I, Edward Snelson, confirm that the work presented in this thesis is my own. Where information ...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
International audienceThis paper describes Gaussian process regression (GPR) models presented in pre...
Gaussian Processes (GPs) are a powerful mod-elling framework incorporating kernels and Bayesian infe...
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to ...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
In this report, we discuss the application and usage of Gaussian Process in Classification and Regre...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Scope of this work Gaussian Process models (GPMs) are extensively used in data analysis given their ...