Sparse Online Gaussian Processes
These pages present a method for probabilistic (Bayesian) inference
using Gaussian Processes (GPs)
main advantage is the combination
of a non-parametric
a probabilistic framework: GPs are probabilistic
The online GP inference provides an approximation to the
posterior process is a GP, meaning that one has the possibility of
assessing posterior uncertainty of predictions. The approximations
are such that they allow a sparse representation of
the posterior and it is applicable to a wide variety of models.
The code for the algorithms is provided as a MATLAB
package and is built on the
freely available NETLAB
(developed by Ian Nabney
which implements general pattern recognition algorithms.
The source code and documentation is available for download in the
these pages. The "Hello World" example program for GP inference, which
is the regression using Gaussian noise is presented and explained there.
Additionally to the simple example programs, the package also includes
two demonstration programs with GUI.
In a first example program (
one generates noisy realisations of the
function. The type and amplitude of the noise and the model
parameters can be set manually.
Using the default settings and generating 40 training points, the
result of learning (before the hyper-parameter adaptation) is
visualised in the image below.
Similarly to the previous section, the program (
illustrates the Sparse GP inference for binary classification.
Questions, comments, suggestions: contact Lehel