Gaussian Processes for Bayesian Hypothesis Tests on Regression Functions
Abstract
Gaussian processes have been used in different application domains such as classification, regression etc. In this paper we show that they can also be employed as a universal tool for developing a large variety of Bayesian statistical hypothesis tests for regression functions. In particular, we will use GPs for testing whether (i) two functions are equal; (ii) a function is monotone (even accounting for seasonality effects); (iii) a function is periodic; (iv) two functions are proportional. By simulation studies, we will show that, beside being more flexible, GP tests are also competitive in terms of performance with state-of-art algorithms.
Cite
Text
Benavoli and Mangili. "Gaussian Processes for Bayesian Hypothesis Tests on Regression Functions." International Conference on Artificial Intelligence and Statistics, 2015.Markdown
[Benavoli and Mangili. "Gaussian Processes for Bayesian Hypothesis Tests on Regression Functions." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/benavoli2015aistats-gaussian/)BibTeX
@inproceedings{benavoli2015aistats-gaussian,
title = {{Gaussian Processes for Bayesian Hypothesis Tests on Regression Functions}},
author = {Benavoli, Alessio and Mangili, Francesca},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2015},
url = {https://mlanthology.org/aistats/2015/benavoli2015aistats-gaussian/}
}