Name: Bayes@Lund 2019
Start: 2019-05-07T08:30:00+02:00
End: 2019-05-07T17:00:00+02:00
Location: Palaestra, Lund University, Sweden

Bayes@Lund 2019

Tuesday, May 7, 2019 - 8:30 AM

Monday, May 6, 2019
Tuesday, May 7, 2019

8:30 AM Bayes@Lund 2019 registration
Bayes@Lund 2019 registration
8:30 AM - 9:00 AM
9:00 AM Welcome - Alexander Holmes (European Spallation Source ERIC)
Welcome
- Alexander Holmes (European Spallation Source ERIC)
9:00 AM - 9:05 AM
9:05 AM Keynote
Keynote
9:05 AM - 9:55 AM
Contributions

9:05 AM Hierarchical models and their applications in astronomy - Maggie Lieu (ESA)
9:55 AM Break
Break
9:55 AM - 10:00 AM
10:00 AM Introducing Bayes for learning
Introducing Bayes for learning
10:00 AM - 10:40 AM
Contributions

10:00 AM What cause successful learning in Bayesian methods? - George Moroz (National Research University Higher School of Economics)

10:20 AM Introducing Bayesian Stats through Signal Detection Theory - Gerit Pfuhl (UiT)
10:40 AM Coffee break
Coffee break
10:40 AM - 11:00 AM
11:00 AM Bayesian inference – a tale of high flexibility
Bayesian inference – a tale of high flexibility
11:00 AM - 12:00 PM
Contributions

11:00 AM Prior thoughts on mixed-membership models in linguistics - Chundra Cathcart

11:20 AM A Bayesian method to localize lost gamma sources - Antanas Bukartas (Lund University)

11:40 AM Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random Fields - Unn Dahlén
12:00 PM Lunch & Mingle
Lunch & Mingle
12:00 PM - 12:45 PM
12:45 PM Keynote & contributed talk
Keynote & contributed talk
12:45 PM - 1:50 PM
Contributions

12:45 PM Visualisation for refining and communicating Bayesian analyses - Robert Grant (BayesCamp)

1:30 PM Bayesian vs. Frequentism for experimentalists - Jakob Lavröd (IYPT Sweden)
1:50 PM Break
Break
1:50 PM - 2:00 PM
2:00 PM Bayesian decisions
Bayesian decisions
2:00 PM - 3:00 PM
Contributions

2:00 PM Extending Bayes to Make Optimal Decisions - Jonas Kristoffer Lindeløv (Department of Communication and Psychology, Aalborg University, Denmark)

2:20 PM Rich-man's Monte Carlo: Uncertainty Analysis in Excel - Dmytro Perepolkin (Lund University)

2:40 PM Bayesian Deep Learning Applications in Biomedicine - Nikolay Oskolkov (Lund University, Department of Biology)
3:00 PM Coffee and cake
Coffee and cake
3:00 PM - 3:30 PM
3:30 PM Bayesian methods for the close to unseen
Bayesian methods for the close to unseen
3:30 PM - 4:50 PM
Contributions

3:30 PM How to deal with a noisy zero – a simple Bayesian treatment for small angle neutron scattering - Alexander Holmes (European Spallation Source ERIC)

3:50 PM Bayesian inference of conformational ensembles from small-angle scattering data - Wojciech Potrzebowski (European Spallation Source, ERIC)

4:10 PM Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers - Andrew McCluskey (Diamond Light Source & University of Bath)

4:30 PM Automatic Learning of Summary Statistics for Approximate Bayesian Computation Using Deep Learning - Samuel Wiqvist (Lund University)
4:50 PM Closing remarks - Ullrika Sahlin (Lund University Centre of Environmental and Climate Research) Rasmus Bååth (Lund University; King Entertainment)
Closing remarks
- Ullrika Sahlin (Lund University Centre of Environmental and Climate Research)
- Rasmus Bååth (Lund University; King Entertainment)
4:50 PM - 5:00 PM