The program for Bayes@Lund2017 is now ready
Follow us at #bayeslund17 on twitter
We have uploaded videos of some of the talks on YouTube. Unfortunately not all talks have been recorded because of technical issues.
Richard McElreath — Bayesian Statistics without Frequentist Language: https://youtu.be/yakg94HyWdE
Martin Stjernman — Joint species modelling. Beautiful in theory, tricky in practice: https://youtu.be/KL9iKx0UKco
Shravan Vasishth — Finite mixture modeling: a case study involving retrieval processes in sentence comprehension: https://youtu.be/02YFuxJmyEI
Mark Andrews –Teaching Bayesian methods to social scientists: https://youtu.be/_gAg9UG9RCA
Stefan Wiens — Making the most of your ANOVAs: From NHST to Bayesian analyses: https://youtu.be/umjX4rnxAm0
Judith Bütepage — Learning to make decisions under uncertainty: https://youtu.be/1nz_96gxFvw
Ullrika Sahlin — Using expert’s knowledge in Bayesian analysis: link to presentation no video unfortunately Sahlin_BayesatLund2017
We start in room MA4, Maths building Annex, Sölvegatan 20. We also have a booklet of the abstracts programBayes@Lund2017 maps and tips.
[for the tutorial on 19 April go here]
08.30-9.00 Registration
09.00-9.10 Welcome and an overview of Bayesian activities in Lund: Umberto Picchini and Ullrika Sahlin
9.10-10.05 Keynote talk Darren Wilkinson: Hierarchical modelling of genetic interaction in budding yeast
10.05-10.30 coffee break
Bayesian Analysis I
10.30-10.55 Stefan Wiens, Making the most of your ANOVAs: From NHST to Bayesian analyses
10.55-11.20 Martin Stjernman, Joint species modelling — beautiful in theory, tricky in practice
11.20-11.45 Shravan Vasishth, Finite mixture modeling: a case study involving retrieval processes in sentence comprehension
11.45-13.05 Lunch break
13.05-14.00 Keynote talk Richard McElreath: Understanding Bayesian statistics without frequentist language
Decisions and Teaching
14.00-14.25 Judith Butepage: Learning to make decisions under uncertainty
14.25-14.50 Mark Andrews: Teaching Bayesian Data Analysis to Social Scientists
14.50-15.10 coffee break
Parallel Sessions
Bayesian Analysis II (room MA7)
15.10-15.35 Thomas Hamelrick: Potentials of mean force for protein structure prediction: from hack to math
15.35-16.00 Junpeng Lao: Statistical Inferences of Eye movement data using Bayesian smoothing
Teaching Bayes (room MA6)
15.10-15.35 Richard Torkar: Convincing researchers to transition to Bayesian statistics – the case of software engineering
15.35-16.00 Bertil Wegmann: Experiences from teaching Bayesian inference to students familiar with frequentist statistics
all back in room MA7 for the final session
Bayesian Analysis III (room MA7)
16.05-16.30 Erik Lindström: Multilevel Monte Carlo methods for inference in multivariate diffusions
16.30-16.55 Ullrika Sahlin: Using expert’s knowledge in Bayesian analysis
Funding from the research schools BECC and COMPUTE is greatly appreciated