Bayes for scattering workshop

Monday 6 May 2019, 12:00 → 17:00 Europe/Zurich

M5 (COBIS)

12:00 → 12:45 Lunch

12:45 → 14:20 Use cases for Bayes in scattering

12:45 Introduction

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

Speaker: Andreas Larsen (University of Copenhagen)

AndreasLarsen.pdf

13:20 A simple bayesian method for 2D SANS

Speaker: Alexander Holmes (European Spallation Source ERIC)

DMSC short.pptx

13:40 Bayes for reflectometry

Speaker: Andrew McCluskey (Diamond Light Source)

bayes_for_scattering2019.pdf

14:00 Bayesian statistic for SAS and beyond

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

14:15 Introduction

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

15:00 Coffee break

15:10 Group discussions

16:00 → 17:00 Wrap/up: Wrap-up

16:00 Report from discussions

Items discussed at the meeting:

1. Bayesian statistics to optimize experiments i.e. when do I have good statistics to stop measuring?
2. How to treat variables correlations
3. Resolution functions for small angle scattering
4. Bayesian model comparison (how to select good model for fitting)
5. Bayesian statistics as way to avoid overfitting
6. Bayesian statistics and its relation to machine and deep learning
7. How to make scattering communities aware of Bayesian statistics: constant presentation, highlight important results, show probability distribution
8. Plotting distributions, Library for plotting
9. Easy accessible software
10. Can we have a "community prior"?
11. Data format for prior and posterior to update

16:30 Next steps