Mixtures of Gaussian processes
V Tresp - Advances in neural information processing …, 2000 - proceedings.neurips.cc
Advances in neural information processing systems, 2000•proceedings.neurips.cc
We introduce the mixture of Gaussian processes (MGP) model which is useful for
applications in which the optimal bandwidth of a map is input dependent. The MGP is
derived from the mixture of experts model and can also be used for modeling general
conditional probability densities. We discuss how Gaussian processes-in particular in form
of Gaussian process classification, the support vector machine and the MGP model (cid:
173) can be used for quantifying the dependencies in graphical models.
applications in which the optimal bandwidth of a map is input dependent. The MGP is
derived from the mixture of experts model and can also be used for modeling general
conditional probability densities. We discuss how Gaussian processes-in particular in form
of Gaussian process classification, the support vector machine and the MGP model (cid:
173) can be used for quantifying the dependencies in graphical models.
Abstract
We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the mixture of experts model and can also be used for modeling general conditional probability densities. We discuss how Gaussian processes-in particular in form of Gaussian process classification, the support vector machine and the MGP model (cid: 173) can be used for quantifying the dependencies in graphical models.
proceedings.neurips.cc
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