Name
Abstract | ||
|---|---|---|
In this work we study the problem of efficient non-parametric estimation for non-linear time-space dynamic Gaussian processes (GP). We propose a systematic and explicit procedure to address this problem by pairing GP regression with Kalman Filtering. Under a specific separability assumption of the modeling kernel and periodic sampling on a (possibly non-uniform) space-grid, we show how to build an exact finite dimensional discrete-time state-space representation for the modeled process. The major finding is that the state at instant k of the associated Kalman Filter represents a sufficient statistic to compute the minimum variance prediction of the process at instant k over any arbitrary finite subset of the space. Finally, we compare the proposed strategy with standard approaches. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | Gaussian regression, machine learning, Kalman filtering, spatio-temporal Gaussian processes |
Field | DocType | ISSN |
Kernel (linear algebra),Minimum-variance unbiased estimator,Extended Kalman filter,Mathematical optimization,Fast Kalman filter,Computer science,Kalman filter,Artificial intelligence,Gaussian process,Ensemble Kalman filter,Sufficient statistic,Machine learning | Conference | 0743-1546 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carron, A. | 1 | 28 | 4.97 |
| Marco Todescato | 2 | 27 | 6.63 |
| Ruggero Carli | 3 | 894 | 69.17 |
| L. Schenato | 4 | 839 | 72.18 |
| Pillonetto Gianluigi | 5 | 877 | 80.84 |