Bayes@Lund2017 20th April

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:
Martin Stjernman — Joint species modelling. Beautiful in theory, tricky in practice:
Shravan Vasishth — Finite mixture modeling: a case study involving retrieval processes in sentence comprehension:
Mark Andrews –Teaching Bayesian methods to social scientists:
Stefan Wiens — Making the most of your ANOVAs: From NHST to Bayesian analyses:
Judith Bütepage — Learning to make decisions under uncertainty:

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

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