A learning procedure takes as input a dataset and performs inference for the parameters θ of
a model that is assumed to have given rise to the dataset. Here we consider learning …

Statistical models can involve implicitly defined quantities, such as solutions to nonlinear
ordinary differential equations (ODEs), that unavoidably need to be numerically …

For over a century, extrapolation methods have provided a powerful tool to improve the
convergence order of a numerical method. However, these tools are not well-suited to …

Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past
decade as uncertainty-aware numerical integrators. They typically proceed by assuming a …

Understanding neural computation on the mechanistic level requires models of neurons and
neuronal networks. To analyze such models one typically has to solve coupled ordinary …

Computationally expensive computer models, known as simulators, are fundamental to the
modern scientific process. In recent years, there has been an increased interest in stochastic …