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Workshop on state of the art in sampling and clustering

→ Europe/Berlin
Zoom, Gather Town and MPP Auditorium (Virtually and at MPP)

Zoom, Gather Town and MPP Auditorium

Virtually and at MPP

Max Planck Institute for Physics, Munich and anywhere on earth via remote connection
Description

Zoom connection information and other details will be sent to participants via Indico reminder emails each morning. Watch out for emails with the subject "[Indico] [Event reminder] Workshop on state of the art in sampling and clustering", check your spam folder as well if necessary. Please contact us if you didn't receive a first such email yet.

The workshop will cover both introductory and advanced topics in the field of statistical sampling and clustering. In addition to lectures on state of the art approaches, the workshop will also comprise hand-on and excercise sessions.

The workshop, hosted by the Max Planck Institute for Physics (MPP)  is organised by the INSIGHTS ITN, MPP IMPRS and the ORIGINS Excellence Cluster, it is open to everybody affiliated with these organizations.

Due to Covid-19, all sessions and social interactions will be held online. Some of the speakers and a limited number of people will participate in person at the MPP auditorium.

Note: Registration for this event is now closed.

Ina Wacker
  • Monday, 5 October
    • 13:00 → 13:10
      Welcome 10m
      Speakers: Allen Caldwell (Max Planck Institute for Physics), Oliver Schulz (Max Planck for Physics)
    • 13:10 → 15:00
      Introduction to Information Field Theory (IFT) 1h 50m
      Speaker: Torsten Enßlin (Max Planck Institute for Astrophysics)
      IFT-2020-10-05.pdf
    • 15:00 → 15:30
      Virtual Social Interaction/Discussion 30m
    • 15:30 → 17:00
      NIFTy – Numerical Information Field Theory 1h 30m

      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.

      Speaker: Philipp Arras
      NIFTy6 introduction
  • Tuesday, 6 October
    • 09:00 → 10:30
      Approximate Bayesian Computation (ABC) 1h 30m
      Speaker: Christian Robert (Université Paris Dauphine PSL)
      ABCourse.pdf
    • 10:30 → 12:00
      Approximate Bayesian Computation (ABC) 1h 30m
      Speaker: Christian Robert (Université Paris Dauphine PSL)
    • 12:00 → 13:30
      Break 1h 30m
    • 13:30 → 14:15
      Statistics in autonomous driving 45m
      Speaker: Frederik Beaujean (MPP)
    • 14:15 → 15:00
      Virtual Social Interaction/Discussion 45m
    • 15:00 → 16:00
      Epidemic Models to Quantify the Effects of Testing, Contact Tracing and Containment 1h
      Speaker: Manuel Gomez Rodriguez (MPI for Software Systems)
  • Wednesday, 7 October
    • 10:00 → 11:30
      Foundations of Clustering 1h 30m
      Speaker: Debarghya Ghoshdastidar (TUM)
      MPP-Clustering-Debarghya.pdf
    • 11:30 → 12:00
      Virtual Social Interaction/Discussion 30m
    • 12:00 → 13:30
      Break 1h 30m
    • 13:30 → 15:00
      Clustering, continued 1h 30m
      Speaker: Debarghya Ghoshdastidar (TUM)
    • 15:00 → 15:30
      Virtual Social Interaction/Discussion 30m
    • 15:30 → 17:00
      Introduction to nested sampling 1h 30m
      Speaker: Johannes Buchner (MPI for Extraterrestrial Physics)
      2020-nested_sampling.pdf
      Tutorial 1
      Tutorial 2
      Tutorial 3
      Tutorial 4
      Tutorial 5
  • Thursday, 8 October
  • Friday, 9 October
    • 10:00 → 11:00
      Updates on BAT.jl, a Bayesian Analysis Toolkit in Julia 1h

      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.

      Speaker: Oliver Schulz (Max Planck for Physics)
      BAT Documentation
      BAT.jl environment
      oschulz-bat.pdf
    • 11:00 → 12:00
      Social Discussion Session 1h
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