Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem. - Matteo Giordano; Richard Nickl (CoRR, 2019)

On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms. - Richard Nickl; Sven Wang (CoRR, 2020)

Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems. - Richard Nickl; Sara A. van de Geer; Sven Wang (SIAM/ASA J. Uncertain. Quantification, 2020)

On free energy barriers in Gaussian priors and failure of MCMC for high-dimensional unimodal distributions. - Afonso S. Bandeira; Antoine Maillard; Richard Nickl; Sven Wang (CoRR, 2022)

Inference for diffusions from low frequency measurements. - Richard Nickl (CoRR, 2022)

On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations. - Richard Nickl; Edriss S. Titi (CoRR, 2023)

Bayesian Nonparametric Inference in McKean-Vlasov models. - Richard Nickl; Grigorios A. Pavliotis; Kolyan Ray (CoRR, 2024)

Bernstein-von Mises theorems for time evolution equations. - Richard Nickl (CoRR, 2024)

On low frequency inference for diffusions without the hot spots conjecture. - Giovanni S. Alberti; Douglas Barnes; Aditya Jambhale; Richard Nickl (CoRR, 2024)

Inferring diffusivity from killed diffusion. - Richard Nickl; Fanny Seizilles (CoRR, 2025)