Bayes@Lund Overlay
Bayes@Lund Overlay highlights new research in the areas of Bayesian data analysis, statistical inference, decision and risk analysis.
Editors Dmytro Perepolkin Ullrika Sahlin
Expert-elicitation method for non-parametric joint priors using normalizing flows (2024)
Florence Bockting, Stefan T. Radev, Paul-Christian Bürkner
http://arxiv.org/abs/2411.15826v1
Nov 28, 2024 - Simulation based elicitation framework to learn flexible (i.e., non-parametric) joint priors for the model parameters
Simulation-based prior knowledge elicitation for parametric Bayesian models (2024)
Florence Bockting, Stefan T. Radev, Paul-Christian Bürkner
http://dx.doi.org/10.1038/s41598-024-68090-7
Jul 31, 2024 - Further development of ideas in Hartman (2020)
Translating predictive distributions into informative priors (2023)
Andrew A. Manderson, Robert J. B. Goudie
http://arxiv.org/abs/2303.08528v1
Mar 18, 2023 - Great follow-up to Hartman et al (2020) through predictive CDF discrepancy and maximization of prior variance.
Flexible Prior Elicitation via the Prior Predictive Distribution (2020)
Marcelo Hartmann, Georgi Agiashvili, Paul Bürkner, Arto Klami
http://arxiv.org/abs/2002.09868v3
Mar 02, 2023 - Very promising new method of inferring the distribution of parameters from the predictive judgments by experts
