Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | JMLR Workshop and Conference Proceedings | Regression,Gaussian process,Global Positioning System,Artificial intelligence,Periodic graph (geometry),Statistical hypothesis testing,Monotone polygon,Machine learning,Mathematics,Bayesian probability |
DocType | Volume | ISSN |
Conference | 38 | 1938-7288 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessio Benavoli | 1 | 229 | 30.52 |
| Francesca Mangili | 2 | 54 | 5.52 |