Welcome

• Allen Caldwell (Max Planck Institute for Physics)
• Oliver Schulz (Max Planck for Physics)

Room: Zoom, Gather Town and MPP Auditorium

Introduction to Information Field Theory (IFT)

• Torsten Enßlin (Max Planck Institute for Astrophysics)

Room: Zoom, Gather Town and MPP Auditorium

NIFTy – Numerical Information Field Theory

• Philipp Arras

Room: Zoom, Gather Town and MPP Auditorium NIFTy "Numerical Information Field Theory", is a versatile library designed to enable the development of signal inference algorithms that are independent of the underlying grids (spatial, spectral, temporal, …) and their resolutions. Its object-oriented framework is written in Python, although it accesses libraries written in C++ and C for efficiency.

Approximate Bayesian Computation (ABC)

• Christian Robert (Université Paris Dauphine PSL)

Room: Zoom, Gather Town and MPP Auditorium

Approximate Bayesian Computation (ABC)

• Christian Robert (Université Paris Dauphine PSL)

Room: Zoom, Gather Town and MPP Auditorium

Statistics in autonomous driving

• Frederik Beaujean (MPP)

Room: Zoom, Gather Town and MPP Auditorium

Epidemic Models to Quantify the Effects of Testing, Contact Tracing and Containment

• Manuel Gomez Rodriguez (MPI for Software Systems)

Room: Zoom, Gather Town and MPP Auditorium

Foundations of Clustering

• Debarghya Ghoshdastidar (TUM)

Room: Zoom, Gather Town and MPP Auditorium

Clustering, continued

• Debarghya Ghoshdastidar (TUM)

Room: Zoom, Gather Town and MPP Auditorium

Introduction to nested sampling

• Johannes Buchner (MPI for Extraterrestrial Physics)

Room: Zoom, Gather Town and MPP Auditorium

Clustering, hands-on

• Philipp Eller (Max Planck for Physics)

Room: Zoom, Gather Town and MPP Auditorium

Introduction to Hamiltonian Monte Carlo (HMC)

• Michael Betancourt

Room: Zoom, Gather Town and MPP Auditorium

More on Hamiltonian Monte Carlo, with exercises

• Michael Betancourt

Room: Zoom, Gather Town and MPP Auditorium

Updates on BAT.jl, a Bayesian Analysis Toolkit in Julia

• Oliver Schulz (Max Planck for Physics)

Room: Zoom, Gather Town and MPP Auditorium BAT.jl is a Bayesian Analysis Toolkit implemented in the Julia language. It is a high high-performance tool box for Bayesian inference with statistical models expressed in a general-purpose programming language, instead of a domain-specific language. Typical applications for this package are the extraction of the values of the parameters of a model, the comparison of different models in the light of a given data set and the test of the validity of a model to represent the data set at hand. BAT.jl provides access to the full Bayesian posterior distribution to enable parameter estimation, limit setting and uncertainty propagation. BAT.jl also provides supporting functionality like plotting recipes and reporting functions.

Social Discussion Session Room: Zoom, Gather Town and MPP Auditorium