Moritz Schauer

Senior Lecturer at Applied Mathematics and Statistics

I am working on statistical theory and methodology for dynamical stochastic models such as stochastic differential equations. In general, dynamical stochastic models describe the evolution of processes and systems which have dynamics with temporal or spatial interactions and show stochastic behaviour. Applications of such models are found in all areas, be it to model the change in the extension of the West Antarctic ice shelf, the interaction of neurons in the brain or the deformation of tissue during tumour growth.

In particular I am interested in statistical inference for nonlinear stochastic differential equations from indirect observation, using Bayesian approaches to inference. I work on finding inference procedures for such models with provably good statistical properties, using modern probability theory and stochastic calculus and the theory of non-parametric Bayesian inference and I work on their computational implementation using advanced Markov Chain Monte Carlo techniques.

Source: chalmers.se
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Showing 5 publications

2021

A piecewise deterministic Monte Carlo method for diffusion bridges

Joris Bierkens, Sebastiano Grazzi, Frank Van Der Meulen et al
Statistics and Computing. Vol. 31 (3)
Journal article
2020

Simulation of elliptic and hypo-elliptic conditional diffusions

Joris Bierkens, Frank van der Meulen, Moritz Schauer
Advances in Applied Probability. Vol. 52 (1), p. 173-212
Journal article
2020

Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient

Shota Gugushvili, Frank van der Meulen, Moritz Schauer et al
Brazilian Journal of Probability and Statistics. Vol. 34 (3), p. 537-579
Journal article
2019

Nonparametric Bayesian inference for Gamma-type Lévy subordinators

Denis Belomestny, Shota Gugushvili, Moritz Schauer et al
Communications in Mathematical Sciences. Vol. 17 (3), p. 781-816
Journal article
2019

Bayesian wavelet de-noising with the caravan prior

Shota Gugushvili, Frank van der Meulen, Moritz Schauer et al
ESAIM - Probability and Statistics. Vol. 23, p. 947-978
Journal article

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Showing 1 research projects

2020–

Stochastic Continuous-Depth Neural Networks

Moritz Schauer Applied Mathematics and Statistics
Annika Lang Applied Mathematics and Statistics
Oskar Eklund Applied Mathematics and Statistics
Chalmers AI Research Centre (CHAIR)

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