# Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions

arXiv: https://arxiv.org/abs/1510.05526 · HTML: https://mlsanity.com/paper/1510.05526/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2015.

## Built on by (most cited first, 5 of 5)

- [Towards a mathematical theory of trajectory inference](https://mlsanity.com/paper/2102.09204/) (2021)
- [Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems](https://mlsanity.com/paper/2405.15676/) (2024)
- [Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence](https://mlsanity.com/paper/2305.15557/) (2023)
- [Statistical Spatially Inhomogeneous Diffusion Inference](https://mlsanity.com/paper/2312.05793/) (2023)
- [Relative Information Gain and Gaussian Process Regression](https://mlsanity.com/paper/2510.04277/) (2025)

## Abstract (arXiv)

We consider nonparametric Bayesian inference in a reflected diffusion model dXt = b (Xt)dt + σ(Xt) dWt, with discretely sampled observations X0, X_Delta, dots, XnDelta. We analyse the nonlinear inverse problem corresponding to the `low frequency sampling' regime where Delta>0 is fixed and n → ∞. A general theorem is proved that gives conditions for prior distributions Pi on the diffusion coefficient σ and the drift function b that ensure minimax optimal contraction rates of the posterior distribution over H"older-Sobolev smoothness classes. These conditions are verified for natural examples of nonparametric random wavelet series priors. For the proofs we derive new concentration inequalities for empirical processes arising from discretely observed diffusions that are of independent interest.