Bounded Gaussian Process Regression
Bjørn S. Jensen, Jens B. Nielsen, Jan Larsen

We extend the Gaussian process (GP) framework for bounded
regression by introducing two bounded likelihood functions
that model the noise on the dependent variable explicitly.
This is fundamentally different from the implicit noise assumption
in the previously suggested warped GP framework.
We approximate the intractable posterior distributions by
the Laplace approximation and expectation propagation and
show the properties of the models on an artificial example.
We finally consider two real-world data sets originating from
perceptual rating experiments which indicate a significant
gain obtained with the proposed explicit noise-model extension.