Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
Robust Gaussian Process Regression with a Student-t Likelihood
The Journal of Machine Learning Research
Information-geometric approach to inferring causal directions
Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model
Journal of Computational Neuroscience
Learning relevance from natural eye movements in pervasive interfaces
Proceedings of the 14th ACM international conference on Multimodal interaction
Engineering Applications of Artificial Intelligence
Sparse gaussian processes for multi-task learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
GPstuff: Bayesian modeling with Gaussian processes
The Journal of Machine Learning Research
Gaussian Kullback-Leibler approximate inference
The Journal of Machine Learning Research
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The 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.