We present a Gaussian process (GP) approach to regression that can
handle data subject to censoring. Since the model is not analytically
tractable we use Expectation propagation to perform approximate inference
on it.
Code
The Matlab code can be downloaded here. It has been tested with the free software GPstuff v4.5. It can also be used with the Laplace approximation.
References
Perry Groot, Peter Lucas.
Gaussian process regression with censored data using Expectation propagation, PGM 2012.
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