skip to content

Statistical Laboratory

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
– Philosophical transactions. Series 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 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
  • 1 of 6
  • >

Frontpage talks

Statistics

Statistics

Probability

Cambridge Statistics Clinic

17
May
14:00 - 15:00: Title to be confirmed
Statistics

Research Group

Statistical Laboratory

Room

D2.05

Telephone

01223 765020