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Cantab Capital Institute for the Mathematics of Information

 

Professor of Mathematical Statistics

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

 

 

Publications

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
– Philos Trans A Math Phys Eng Sci
(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)
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 NoisyNon-Abelian X-RayTransforms
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
On statistical Calderón problems
K Abraham, R Nickl
– Mathematical Statistics and Learning
(2020)
2,
165
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Research Group

Cantab Capital Institute for the Mathematics of Information

Room

D2.05

Telephone

01223 765020