Abstract
We study the performance of nonparametric Bayes procedures for one-dimensional diffusions with periodic drift. We improve existing convergence rate results for Gaussian process (GP) priors with fixed hyper parameters. Moreover, we exhibit several possibilities to achieve adaptation to smoothness. We achieve this by considering hierarchical procedures that involve either a prior on a multiplicative scaling parameter, or a prior on the regularity parameter of the GP.
Original language | English |
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Pages (from-to) | 628-645 |
Number of pages | 18 |
Journal | Electronic Journal of Statistics |
Volume | 10 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Externally published | Yes |
Keywords
- Adaptation to smoothness
- Asymptotic performance
- Bayesian inference
- Gaussian process prior
- Nonparametric inference for diffusions