Moritz Schauer
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.
Showing 16 publications
Conditioning continuous-time Markov processes by guiding
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Nonparametric Bayesian volatility learning under microstructure noise
Weak solutions to gamma-driven stochastic differential equations
Sticky PDMP samplers for sparse and local inference problems
Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
Applied measure theory for probabilistic modeling.
Automatic Differentiation of Programs with Discrete Randomness
Diffusion Bridges for Stochastic Hamiltonian Systems and Shape Evolutions
Continuous-discrete smoothing of diffusions
A piecewise deterministic Monte Carlo method for diffusion bridges
Simulation of elliptic and hypo-elliptic conditional diffusions
Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient
Bayesian wavelet de-noising with the caravan prior
Nonparametric Bayesian volatility estimation
Nonparametric Bayesian inference for Gamma-type Lévy subordinators
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Showing 1 research projects
Stochastic Continuous-Depth Neural Networks