Add to ORKGI The flexibility of Student-t processes (TPs) over Gaussian processes (GPs) robustifies inference in noisy data [1,2]. I Predictive covariances explicitly depend on th
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
The added flexibility of Student-t processes (TPs) over Gaussian processes (GPs) robus-tifies infere...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Contains fulltext : 129969.pdf (publisher's version ) (Closed access)This paper co...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
We propose the Laplace method to derive approximate inference for Gaussian process (GP) regression i...
We propose the Laplace method to derive approximate inference for Gaussian process (GP) regression i...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
Based on a statistical mechanics approach, we develop a method for approximately computing average c...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
The added flexibility of Student-t processes (TPs) over Gaussian processes (GPs) robus-tifies infere...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric p...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Contains fulltext : 129969.pdf (publisher's version ) (Closed access)This paper co...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
We propose the Laplace method to derive approximate inference for Gaussian process (GP) regression i...
We propose the Laplace method to derive approximate inference for Gaussian process (GP) regression i...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
Based on a statistical mechanics approach, we develop a method for approximately computing average c...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...