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Statistical Laboratory

Publications

Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors
F Monard, R Nickl, GP Paternain
– 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
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions
R Nickl, K Ray
– Annals of Statistics
(2020)
48,
1383
Bernstein-von Mises theorems for statistical inverse problems I: Schrodinger equation
R Nickl
– Journal of the European Mathematical Society
(2020)
22,
2697
Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems
R Nickl, S Van De Geer, S Wang
– SIAM/ASA Journal on Uncertainty Quantification
(2020)
8,
374
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Loffler, R Nickl
– The Annals of Statistics
(2020)
48,
464
Efficient estimation of linear functionals of principal components
V Koltchinskii, M Löffler, R Nickl
– Annals of Statistics
(2020)
48,
464
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Frontpage talks

18
Jan
Cambridge Statistics Clinic

17
Oct
14:00 - 15:00: Title to be confirmed
Probability

18
Oct
17:00 - 18:00: Title to be confirmed
Peter Whittle Lecture

Research Group

Statistical Laboratory

Room

D2.05

Telephone

01223 765020