Bayes meets Bamberg: Workshop on Bayesian Statistics
INVITATION AND EXTENDED CALL FOR PAPERS for “Bayes meets Bamberg: Workshop on Bayesian Statistics” on 22-23 June 2023 (new date!) at the University of Bamberg
Please send an email to workshop.stat-oek(at)uni-bamberg.de if you want to attend, with the reference „BMB workshop registration“. Registration is free of charge, but we kindly ask you to notify us if you cannot participate and have to withdraw your registration. The maximum participant number is capped at 50 and registrations are handled on a first-come, first-served basis.
General information on the workshop topic
Bayesian Statistics provide an intuitive approach to interpreting what we can learn from data, and how we can update prior knowledge. With growing access to easy-to-use software for Bayesian methods, this approach has become well established within the scientific community. This workshop provides a forum for an interdisciplinary exchange of Bayesian applications and ideas.
Program and course of the workshop
The workshop is a two-day workshop consisting of two parts, with the first day being a “gentle” introduction to Bayesian concepts for anyone who is interested in but not yet quite familiar with Bayesian Statistics. The second day consists of a keynote and sessions with presentations on applied Bayesian research.
Day 1: A Primer on Bayesian Statistics
David Kaplan will give an introduction to Bayesian Statistics on day 1 of the workshop. He is Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin – Madison, and has published extensively on Bayesian methods.
Day 2: Bayesian Statistics in Practice
- Keynote by David Kaplan: Probabilistic Forecasting with International Large-Scale Assessments: Applications to the UN Sustainable Development Goals
Mariana Nold: Bayesian methods for assessing model uncertainty in predictive modeling
Julius Goes: An Introduction to NIMBLE
Tobias Eilert/ Miguel Cordero: A Modern Approach to Stability Studies via Bayesian Linear Mixed Models Incorporating Auxiliary Effects
Georg Heinze: Shrinkage of prediction models: Firth’s logistic regression with added covariate