Nonparametric Bayesian Density Modeling with Gaussian Processes
نویسندگان
چکیده
The Gaussian process is a useful prior on functions for Bayesian kernel regression and classification. Density estimation with a Gaussian process prior is difficult, however, as densities must be nonnegative and integrate to unity. The statistics community has explored the use of a logistic Gaussian process for density estimation, relying on approximations of the normalization constant (e.g. [1, 2, 3]).
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تاریخ انتشار 2008