Probabilistic Numerics for ODEs 9: A practical showcase
Channel: Tübingen Machine Learning
Date: 23/07/2021

This video is part of a ten-part spotlight series on Probabilistic Numerical Methods for (ordinary) differential equations.

In this penultimate video, Jonathan Schmidt uses the concrete example of modelling latent contact rates in an SIRD model of the Covid19 pandemic to show that probabilistic ODE filters, using the extended Kalman filter, can approximately infer nonparametric latent forces in a single forward solve.

More information can be found in https://arxiv.org/abs/2103.10153

The probnum package is under active development at http://probnum.org.