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Department of Pure Mathematics and Mathematical Statistics

 

Professor of Mathematical Statistics

Research Interests: Mathematical Statistics; specifically high-dimensional inference, Bayesian nonparametrics, statistics for PDEs and inverse problems, empirical process theory.

 

 

Publications

Bayesian Nonparametric Inference in McKean-Vlasov models
R Nickl, GA Pavliotis, K Ray
(2024)
On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations
R Nickl, ES Titi
(2023)
On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions.
AS Bandeira, A Maillard, R Nickl, S Wang
– Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
(2023)
381,
20220150
On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms
R Nickl, S Wang
– Journal of the European Mathematical Society
(2022)
26,
1031
Consistent inference for diffusions from low frequency measurements
R Nickl
(2022)
STATISTICAL GUARANTEES for BAYESIAN UNCERTAINTY QUANTIFICATION in NONLINEAR INVERSE PROBLEMS with GAUSSIAN PROCESS PRIORS
F Monard, R Nickl, GP Paternain
– The Annals of Statistics
(2021)
49,
3255
On some information-theoretic aspects of non-linear statistical inverse problems
R Nickl, G Paternain
(2021)
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems
J Bohr, R Nickl
(2021)
Consistent Inversion of Noisy Non‐Abelian X‐Ray Transforms
F Monard, R Nickl, GP Paternain
– Communications on Pure and Applied Mathematics
(2020)
74,
1045
Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem
M Giordano, R Nickl
– Inverse Problems
(2020)
36,
085001
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Room

D2.05

Telephone

01223 765020