Add to ORKGGaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method
The main challenges that arise when adopting Gaussian process priors in probabilistic modeling are h...
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are h...
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced....
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortu...
We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian ...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Gaussian processes are powerful nonparametric distributions over continuous functions that have beco...
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and c...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling cap...
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian p...
Analyzing latent Gaussian models by using approximate Bayesian inference methods has proven to be a ...
We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for...
Abstract—Kernel methods have revolutionized the fields of pattern recognition and machine learning. ...
The main challenges that arise when adopting Gaussian process priors in probabilistic modeling are h...
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are h...
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced....
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortu...
We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian ...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Gaussian processes are powerful nonparametric distributions over continuous functions that have beco...
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and c...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling cap...
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian p...
Analyzing latent Gaussian models by using approximate Bayesian inference methods has proven to be a ...
We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for...
Abstract—Kernel methods have revolutionized the fields of pattern recognition and machine learning. ...
The main challenges that arise when adopting Gaussian process priors in probabilistic modeling are h...
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are h...
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced....