Probabilistic Numerics (SIAM UQ 2020)

Technical University Munich, Germany, 24 - 27 March 2020

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)

The minisymposia are organized by Alex Diaz, Chris Oates, Toni Karvonen, Alexandra Gessner, Philipp Hennig, and Tim Sullivan.