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Bayesian Inference in High-Energy Physics

25–27 May 2022
IPPP
Europe/London timezone

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Contribution List

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  1. 20. Durham Locals Session: Intro to Bayesian Inference (for our local PhD / PostDoc / Staff)
    25/05/2022, 10:00

    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
    25/05/2022, 13:55
  3. 2. Mapping Machine-Learned Physics into a Human-Readable Space
    Taylor Faucett (UC, Irvine)
    25/05/2022, 14:00
  4. 3. Bayesian Inference of QCD splittings with Invertible Networks
    Theo Heimel (U. Heidelberg, ITP)
    25/05/2022, 14:35
  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)
    25/05/2022, 15:10
  6. 6. A graphical multi-fidelity Gaussian process model, with application to emulation of expensive computer simulations
    Steffen Bass (Duke University)
    25/05/2022, 16:15
  7. 7. Exploring phase space with Nested Sampling
    David Yallup (University of Cambridge)
    25/05/2022, 16:50
  8. 8. Fully Bayesian parameter inference for Markov processes
    Darren Wilkinson (Department of Mathematical Sciences at Durham University)
    26/05/2022, 09:00
  9. 9. Bayes linear strategies for emulation and history matching for complex computer models
    Michael Goldstein (Department of Mathematical Sciences at Durham University)
    26/05/2022, 09:40
  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)
    26/05/2022, 10:20
  11. 10. Enforcing stationarity through the prior in vector autoregressions
    Sarah Heaps (Department of Mathematical Sciences at Durham University)
    26/05/2022, 11:20
  12. 11. Contemporaneous MCMC
    Louis Aslett (Department of Mathematical Sciences at Durham University)
    26/05/2022, 12:00
  13. 13. Differentiable programming and its applications
    Lukas Heinrich (Technical University Munich)
    26/05/2022, 14:40
  14. 14. HEP Simulator issues tackled with Bayesian Inference
    Manuel Szewc (IJS & ICAS-UNSAM)
    26/05/2022, 15:20
  15. 15. Quantifying Uncertainty in Deep Learning
    Mike Walmsley (University of Manchester)
    26/05/2022, 16:30
  16. 16. The inverse problem challenge in lattice QCD
    Alexander Rothkopf (University of Stavanger)
    26/05/2022, 17:10
  17. 17. Causal Inference
    Ezequiel Alvarez (ICAS, UNSAM)
    27/05/2022, 09:00
  18. 18. Representation Learning for HEP
    Barry Dillon (University of Heidelberg)
    27/05/2022, 09:40
  19. 19. Bayesian community criteria for anomaly evaluation
    Jernej Kamenik (Jozef Stefan Institute & Ljubljana University)
    27/05/2022, 10:20