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Workshop on state of the art in sampling and clustering

Oct 5 – 9, 2020
Virtually and at MPP
Europe/Berlin timezone

Ina Wacker

Contribution List

20 out of 20 displayed
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  1. 2. Welcome
    Allen Caldwell (Max Planck Institute for Physics), Oliver Schulz (Max Planck for Physics)
    10/5/20, 1:00 PM
  2. 3. Introduction to Information Field Theory (IFT)
    Torsten Enßlin (Max Planck Institute for Astrophysics)
    10/5/20, 1:10 PM
  3. 4. NIFTy – Numerical Information Field Theory
    Philipp Arras
    10/5/20, 3:30 PM

    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.

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  4. 6. Approximate Bayesian Computation (ABC)
    Christian Robert (Université Paris Dauphine PSL)
    10/6/20, 9:00 AM
  5. 7. Approximate Bayesian Computation (ABC)
    Christian Robert (Université Paris Dauphine PSL)
    10/6/20, 10:30 AM
  6. 10. Statistics in autonomous driving
    Frederik Beaujean (MPP)
    10/6/20, 1:30 PM
  7. 20. Epidemic Models to Quantify the Effects of Testing, Contact Tracing and Containment
    Manuel Gomez Rodriguez (MPI for Software Systems)
    10/6/20, 3:00 PM
  8. 12. Foundations of Clustering
    Debarghya Ghoshdastidar (TUM)
    10/7/20, 10:00 AM
  9. 19. Clustering, continued
    Debarghya Ghoshdastidar (TUM)
    10/7/20, 1:30 PM
  10. 18. Introduction to nested sampling
    Johannes Buchner (MPI for Extraterrestrial Physics)
    10/7/20, 3:30 PM
  11. 13. Clustering, hands-on
    Philipp Eller (Max Planck for Physics)
    10/8/20, 10:00 AM
  12. 14. Introduction to Hamiltonian Monte Carlo (HMC)
    Michael Betancourt
    10/8/20, 1:30 PM
  13. 15. More on Hamiltonian Monte Carlo, with exercises
    Michael Betancourt
    10/8/20, 3:30 PM
  14. 5. Updates on BAT.jl, a Bayesian Analysis Toolkit in Julia
    Oliver Schulz (Max Planck for Physics)
    10/9/20, 10:00 AM

    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...

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  15. 17. Social Discussion Session
    10/9/20, 11:00 AM
  16. 9. Exercise session
  17. 11. Free time to work on content of previous lectures
  18. 8. Interactive Discussions
  19. 16. Q&A with lecturers, discussion on cutting-edge problem
  20. 1. Registration