Name
Title | ||
|---|---|---|
Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein–Uhlenbeck Prior |
Abstract | ||
|---|---|---|
This article studies the maximum likelihood estimates of magnitude and scale parameters for a Gaussian process of Ornstein-Uhlenbeck type used to model a deterministic function that does not have to be a realisation of an Ornstein- Uhlenbeck process. Specifically, we derive explicit expressions for the limiting values of the maximum likelihood estimates as the number of observations increases. The results demonstrate that the function typically needs to be sufficiently similar to a sample path of an Ornstein-Uhlenbeck process or have discontinuities if the variance of the model is to remain non-zero. Numerical examples illustrate the behaviour of the estimates when the function is not a sample path of any Ornstein-Uhlenbeck process. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MLSP.2019.8918767 | 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Gaussian process regression,Ornstein– Uhlenbeck process,maximum likelihood estimation,probabilistic numerics | Conference | 1551-2541 |
ISBN | Citations | PageRank |
978-1-7281-0825-4 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karvonen, Toni | 1 | 11 | 6.65 |
| Filip Tronarp | 2 | 8 | 5.65 |
| Simo Särkkä | 3 | 623 | 66.52 |