Mixtures of Gaussian Processes
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.
Cite
Text
Tresp. "Mixtures of Gaussian Processes." Neural Information Processing Systems, 2000.Markdown
[Tresp. "Mixtures of Gaussian Processes." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/tresp2000neurips-mixtures/)BibTeX
@inproceedings{tresp2000neurips-mixtures,
title = {{Mixtures of Gaussian Processes}},
author = {Tresp, Volker},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {654-660},
url = {https://mlanthology.org/neurips/2000/tresp2000neurips-mixtures/}
}