# Moritz Schauer

Associate senior lecturer, Mathematical Sciences

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.

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.

See my homepage http://www.math.chalmers.se/~smoritz/index.html for more information.