Similarity of individual latent dynamics in longitudinal cohort data (A03)

We will extend our approaches for patient-specific dynamic parameters in a latent representation, based on neural networks and differential equations, to incorporate switches between measurement instruments and treatment regimes. For better reflecting unobserved influences, we will consider stochastic differential equations. Development of the corresponding filtering theory will incorporate random and state-dependent observation time points. We will derive error bounds for the estimator of the latent state, which will also serve as a basis for locally performing model selection.

DFG Programme Collaborative Research Centres

Subproject of SFB 1597:  Small Data

Applicant Institution Albert-Ludwigs-Universität Freiburg

Project Heads Professor Dr. Harald Binder; Professor Dr. Thorsten Schmidt