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Bayesian Inference in High-Energy Physics

May 25 – 27, 2022
IPPP
Europe/London timezone

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  1. 20. Durham Locals Session: Intro to Bayesian Inference (for our local PhD / PostDoc / Staff)
    5/25/22, 10:00 AM

    Attendance from workshop participants is welcome but not expected. This session is aimed at introducing some of the basic ideas of Bayesian Inference to our local PhD/ PostDocs and interested staff members.

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  2. 21. Workshop Official Start & Welcome
    5/25/22, 1:55 PM
  3. 2. Mapping Machine-Learned Physics into a Human-Readable Space
    Taylor Faucett (UC, Irvine)
    5/25/22, 2:00 PM
  4. 3. Bayesian Inference of QCD splittings with Invertible Networks
    Theo Heimel (U. Heidelberg, ITP)
    5/25/22, 2:35 PM
  5. 4. How well do we know the neutron-matter equation of state at the densities inside neutron stars? A Bayesian approach with correlated uncertainties
    Jordan Melendez (The Ohio State University)
    5/25/22, 3:10 PM
  6. 6. A graphical multi-fidelity Gaussian process model, with application to emulation of expensive computer simulations
    Steffen Bass (Duke University)
    5/25/22, 4:15 PM
  7. 7. Exploring phase space with Nested Sampling
    David Yallup (University of Cambridge)
    5/25/22, 4:50 PM
  8. 8. Fully Bayesian parameter inference for Markov processes
    Darren Wilkinson (Department of Mathematical Sciences at Durham University)
    5/26/22, 9:00 AM
  9. 9. Bayes linear strategies for emulation and history matching for complex computer models
    Michael Goldstein (Department of Mathematical Sciences at Durham University)
    5/26/22, 9:40 AM
  10. 12. Multilevel Emulation of Complex Computer Models, with application to EAGLE, a Galaxy Formation Simulation
    Ian Vernon (Department of Mathematical Sciences at Durham University)
    5/26/22, 10:20 AM
  11. 10. Enforcing stationarity through the prior in vector autoregressions
    Sarah Heaps (Department of Mathematical Sciences at Durham University)
    5/26/22, 11:20 AM
  12. 11. Contemporaneous MCMC
    Louis Aslett (Department of Mathematical Sciences at Durham University)
    5/26/22, 12:00 PM
  13. 13. Differentiable programming and its applications
    Lukas Heinrich (Technical University Munich)
    5/26/22, 2:40 PM
  14. 14. HEP Simulator issues tackled with Bayesian Inference
    Manuel Szewc (IJS & ICAS-UNSAM)
    5/26/22, 3:20 PM
  15. 15. Quantifying Uncertainty in Deep Learning
    Mike Walmsley (University of Manchester)
    5/26/22, 4:30 PM
  16. 16. The inverse problem challenge in lattice QCD
    Alexander Rothkopf (University of Stavanger)
    5/26/22, 5:10 PM
  17. 17. Causal Inference
    Ezequiel Alvarez (ICAS, UNSAM)
    5/27/22, 9:00 AM
  18. 18. Representation Learning for HEP
    Barry Dillon (University of Heidelberg)
    5/27/22, 9:40 AM
  19. 19. Bayesian community criteria for anomaly evaluation
    Jernej Kamenik (Jozef Stefan Institute & Ljubljana University)
    5/27/22, 10:20 AM