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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
– The Annals of Statistics
(2021)
49,
3255
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
– The Annals of Statistics
(2020)
48,
1383
Bernstein–von Mises theorems for statistical inverse problems I: Schrödinger 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
Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems
A Carpentier, J Eisert, D Gross, R Nickl
– pp
(2019)
3,
385
Efficient nonparametric Bayesian inference for $X$-ray transforms
F Monard, R Nickl, GP Paternain
– The Annals of Statistics
(2019)
47,
1113
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Frontpage talks

09
Feb
Cambridge Statistics Clinic

11
Feb
14:00 - 15:00: TBC
Statistics

18
Feb
14:00 - 15:00: TBC
Statistics

23
Feb
Cambridge Statistics Clinic

Research Group

Statistical Laboratory

Room

D2.05

Telephone

01223 765020