Name
Abstract | ||
|---|---|---|
The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the difference between input samples. These assumptions can be too restrictive and unrealistic for many real-world problems. Although nonstationarity ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TPAMI.2015.2452914 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Approximation methods,Standards,Noise,Training,Computational modeling,Approximation algorithms,Noise measurement | Applied mathematics,Markov chain Monte Carlo,Laplace's method,Artificial intelligence,Gaussian process,Expectation propagation,Covariance,Slice sampling,Approximation algorithm,Pattern recognition,Statistics,Prior probability,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 3 | 0162-8828 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Muñoz-González | 1 | 82 | 8.48 |
| Miguel Lázaro-Gredilla | 2 | 383 | 26.46 |
| Aníbal R. Figueiras-Vidal | 3 | 467 | 38.03 |