Gaussian Process Regression with Censored Data Using Expectation Propagation
نویسندگان
چکیده
Censoring is a typical problem of data gathering and recording. Specialized techniques are needed to deal with censored (regression) data. Gaussian processes are Bayesian nonparametric models that provide state-of-the-art performance in regression tasks. In this paper we propose an extension of Gaussian process regression models to data in which some observations are subject to censoring. Since the model is not analytically tractable we use Expectation propagation to perform approximate inference on it.
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