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, 7 May 2019 - 08:30

Monday, 6 May 2019
Tuesday, 7 May 2019

08:30 Bayes@Lund 2019 registration
Bayes@Lund 2019 registration
08:30 - 09:00
09:00 Welcome - Alexander Holmes (European Spallation Source ERIC)
Welcome
- Alexander Holmes (European Spallation Source ERIC)
09:00 - 09:05
09:05 Keynote
Keynote
09:05 - 09:55
Contributions

09:05 Hierarchical models and their applications in astronomy - Maggie Lieu (ESA)
09:55 Break
Break
09:55 - 10:00
10:00 Introducing Bayes for learning
Introducing Bayes for learning
10:00 - 10:40
Contributions

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

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

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

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

11:40 Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random Fields - Unn Dahlén
12:00 Lunch & Mingle
Lunch & Mingle
12:00 - 12:45
12:45 Keynote & contributed talk
Keynote & contributed talk
12:45 - 13:50
Contributions

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

13:30 Bayesian vs. Frequentism for experimentalists - Jakob Lavröd (IYPT Sweden)
13:50 Break
Break
13:50 - 14:00
14:00 Bayesian decisions
Bayesian decisions
14:00 - 15:00
Contributions

14:00 Extending Bayes to Make Optimal Decisions - Jonas Kristoffer Lindeløv (Department of Communication and Psychology, Aalborg University, Denmark)

14:20 Rich-man's Monte Carlo: Uncertainty Analysis in Excel - Dmytro Perepolkin (Lund University)

14:40 Bayesian Deep Learning Applications in Biomedicine - Nikolay Oskolkov (Lund University, Department of Biology)
15:00 Coffee and cake
Coffee and cake
15:00 - 15:30
15:30 Bayesian methods for the close to unseen
Bayesian methods for the close to unseen
15:30 - 16:50
Contributions

15:30 How to deal with a noisy zero – a simple Bayesian treatment for small angle neutron scattering - Alexander Holmes (European Spallation Source ERIC)

15:50 Bayesian inference of conformational ensembles from small-angle scattering data - Wojciech Potrzebowski (European Spallation Source, ERIC)

16:10 Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers - Andrew McCluskey (Diamond Light Source & University of Bath)

16:30 Automatic Learning of Summary Statistics for Approximate Bayesian Computation Using Deep Learning - Samuel Wiqvist (Lund University)
16:50 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)
16:50 - 17:00