Name: Bayes for scattering workshop
Start: 2019-05-06T12:00:00+02:00
End: 2019-05-06T17:00:00+02:00
Location: COBIS

- 12:00 → 12:45
  
  Lunch 45m
- 12:45 → 14:20
  Use cases for Bayes in scattering
  - 12:45
    
    Introduction 15m
    
    Why are we having this meeting?
    Brief intro to Bayes' theorem.
    
    DMSC bayes intro.pptx
  - 13:00
    
    Analysis of Small-Angle Scattering Data Using Model Fitting and Bayesian Regularization 20m
    
    Speaker: Andreas Larsen (University of Copenhagen)
    
    AndreasLarsen.pdf
  - 13:20
    
    A simple bayesian method for 2D SANS 20m
    
    Speaker: Alexander Holmes (European Spallation Source ERIC)
    
    DMSC short.pptx
  - 13:40
    
    Bayes for reflectometry 20m
    
    Speaker: Andrew McCluskey (Diamond Light Source)
    
    bayes_for_scattering2019.pdf
  - 14:00
    
    Bayesian statistic for SAS and beyond 10m
    
    As a primer for discussion we will try to cover a few more SAS cases as well as other scattering techniques (particularly spectroscopy)
    
    Speaker: Wojciech Potrzebowski (European Spallation Source ERIC)
    
    Bayesian_for_SAS_and_beyond.pptx
- 14:00 → 16:00
  Discussion
  - 14:10
    
    Break 5m
  - 14:15
    
    Introduction 10m
    
    We will try out OST as at the NSS retreat
    https://confluence.esss.lu.se/display/SD/Science+Retreat+2018+-+Fundamentals+of+OST
    Think of topics before.
    Ideas
    Use cases
    Workflow of data analysis
    Presenting results
    Computational techniques
  - 14:25
    
    Group discussions 35m
  - 15:00
    
    Coffee break 10m
  - 15:10
    
    Group discussions 50m
- 16:00 → 17:00
  Wrap/up: Wrap-up
  - 16:00
    Report from discussions 30m
    
    Items discussed at the meeting:
    
    Bayesian statistics to optimize experiments i.e. when do I have good statistics to stop measuring?
    
    How to treat variables correlations
    
    Resolution functions for small angle scattering
    
    Bayesian model comparison (how to select good model for fitting)
    
    Bayesian statistics as way to avoid overfitting
    
    Bayesian statistics and its relation to machine and deep learning
    
    How to make scattering communities aware of Bayesian statistics: constant presentation, highlight important results, show probability distribution
    
    Plotting distributions, Library for plotting
    
    Easy accessible software
    
    Can we have a "community prior"?
    
    Data format for prior and posterior to update
  - 16:30
    
    Next steps 30m

Bayes for scattering workshop

M5

COBIS