research
∙
07/21/2023
Bernstein approximation and beyond: proofs by means of elementary probability theory
Bernstein polynomials provide a constructive proof for the Weierstrass a...
share
research
∙
04/28/2023
Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation
We study numerical integration over bounded regions in ℝ^s, s≥1 with res...
research
∙
03/07/2023
Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems
We develop new multilevel Monte Carlo (MLMC) methods to estimate the exp...
research
∙
03/05/2023
Self-reinforced polynomial approximation methods for concentrated probability densities
Transport map methods offer a powerful statistical learning tool that ca...
research
∙
01/24/2023
Tensor-based Methods for Sequential State and Parameter Estimation in State Space Models
Numerous real-world applications involve the filtering problem: one aims...
research
∙
09/05/2022
A variational neural network approach for glacier modelling with nonlinear rheology
In this paper, we propose a mesh-free method to solve full stokes equati...
research
∙
09/05/2022
Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation
We propose a deep importance sampling method that is suitable for estima...
research
∙
03/07/2022
Convergence Speed and Approximation Accuracy of Numerical MCMC
When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbati...
research
∙
06/08/2021
Conditional Deep Inverse Rosenblatt Transports
We present a novel offline-online method to mitigate the computational b...
research
∙
02/26/2021
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems
Identifying a low-dimensional informed parameter subspace offers a viabl...
research
∙
01/26/2021
Identification of brain states, transitions, and communities using functional MRI
Brain function relies on a precisely coordinated and dynamic balance bet...
research
∙
01/07/2021
A unified performance analysis of likelihood-informed subspace methods
The likelihood-informed subspace (LIS) method offers a viable route to r...
research
∙
09/17/2020
Randomized Reduced Forward Models for Efficient Metropolis–Hastings MCMC, with Application to Subsurface Fluid Flow and Capacitance Tomography
Bayesian modelling and computational inference by Markov chain Monte Car...
research
∙
07/14/2020
Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports
Characterising intractable high-dimensional random variables is one of t...
research
∙
02/15/2020
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems
In many hierarchical inverse problems, not only do we want to estimate h...
research
∙
10/28/2019
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems
We present a non-trivial integration of dimension-independent likelihood...
research
∙
03/03/2019
Scalable optimization-based sampling on function space
Optimization-based samplers provide an efficient and parallellizable app...
research
∙
01/23/2019
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) off...
research
∙
11/23/2018
Semivariogram Hyper-Parameter Estimation for Whittle-Matérn Priors in Bayesian Inverse Problems
We present a detailed mathematical description of the connection between...
research
∙
09/10/2018
Adaptive Approximation Error Models for Efficient Uncertainty Quantification with Application to Multiphase Subsurface Fluid Flow
Sample-based Bayesian inference provides a route to uncertainty quantifi...
research
∙
07/02/2018
Certified dimension reduction in nonlinear Bayesian inverse problems
We propose a dimension reduction technique for Bayesian inverse problems...
research
∙
06/08/2018