David Augustin

This paper introduces filter inference, a scalable method for estimating NLME model parameters from snapshot time series data, enabling efficient analysis of large datasets.

The paper shows that model selection based on AIC or model averaging can lead to unreliable treatment response predictions due to nonlinear dynamics, and proposes probabilistic averaging as a more reliable approach.