Approximate Bayesian inference and model selection for stochastic differential equations (SDEs) (A06)

• Subject Area: Mathematics
• Term: since 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318763901

This project is concerned with Bayesian semi-parametric and fully nonparametric approaches for estimating drift functions in systems of stochastic differential equations (SDE) and dynamical point process models. We will develop efficient sequential Monte Carlo approaches and variational Bayesian methods for this problem and study their convergence rates and approximation properties. We will also derive new methods for Bayesian model selection to decide - on the basis of available data - which prior from a given collection can be efficiently used for the SDE problem at hand.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1294: Data Assimilation – The Seamless Integration of Data and Models
• Applicant Institution: Universität Potsdam
• Project Heads: Professor Dr. Manfred Opper; Professor Dr.-Ing. Sebastian Reich; Professor Dr. Vladimir Spokoiny