Four minisymposia on probabilistic numerical methods were scheduled at the SIAM Conference on Uncertainty Quantification 2020 to be held in Garching near Munich in Southern Germany from 24 to 27 March 2020. Unfortunately, the conference had to be canceled due to the outbreak of COVID-19. Nevertheless, many speakers provided a recording of their presentation. Huge thanks to all contributors, despite the tough circumstances!

Probabilistic Numerical Methods for Cubature


The Successes and Challenges of Automatic Bayesian Cubature
Fred J. Hickernell (Illinois Institute of Technology, US)



Gaussian processes and Uncertainty Quantification
Toni Karvonen (Alan Turing Institute, UK)



Integrals of Linearly Constrained Gaussians
Alexandra Gessner (University of Tübingen, Germany)



Adaptivity in Bayesian Cubature
Matthew Fisher (Newcastle University, UK)



Probabilistic Numerical Methods: Opportunities and Challenges


Probabilistic Numerics: History and Recent Trends
Tim Sullivan (Free University of Berlin, Germany)

It is Time to Take Uncertainty Seriously
Philipp Hennig (University of Tübingen, Germany)



Fast Bayesian Inference for Differential Equations Using Probabilistic Numerical Methods
Chris Oates (Newcastle Univeristy, UK)



Active Multi-Source Bayesian Quadrature for Expensive Simulations
Maren Mahsereci (Amazon Research, UK)

Probabilistic Numerical Methods for Differential Equations and Linear Algebra, Part I


Prior Distributions and Test Statistics for the Bayesian Conjugate Gradient Method
Tim Reid (North Carolina State University, US)



A Probabilistic View on Sparse Cholesky Factorization
Florian Schäfer ( California Institute of Technology, US)



Estimation of Ordinary Differential Equation Models with Discretization Error Quantification
Takeru Matsuda (University of Tokyo, Japan)



Probabilistic Rare-Event Simulation
Alejandro Diaz (University College London, UK)

Probabilistic Numerical Methods for Differential Equations and Linear Algebra, Part II


On the Role of Exponential Integrability of Probabilistic Integrators for Approximate Bayesian Inference
Han Cheng Lie ( University of Potsdam, Germany)



BVPs, Computational Pipelines and a Probabilistic Numerics GOODE
Michael Schober (Bosch, Germany)

Probabilistic Solutions to Ordinary Differential Equations as Non-Linear Bayesian Filtering and Smoothing: Gaussian Approximation
Filip Tronarp (University of Tübingen, Germany)



Approximate Bayesian Solutions to Nonlinear Differential Equations
Junyang Wang (Newcastle Univeristy, UK)