Bayesian social science conference in Amsterdam! Next month!

E. J. Wagenmakers writes:

This year Maarten Marsman and I are the local coordinators of the workshop Bayesian Methods for the Social Sciences II. We hope to organize this conference every two years, alternating between Paris and Amsterdam.

We have another great line-up of speakers this year, and we’d like to spread the word to a larger audience.

The conference takes place 16-18 October 2024 in Amsterdam.

Here’s the list of scheduled talks:

Merlise Clyde (Duke): Estimating Posterior Model Probabilities via Bayesian Model Based Sampling Marie Perrot-Dockès (Université Paris Cité): Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator Joris Mulder (Tilburg): An empirical Bayes factor for testing random effects

Monica Alexander (Toronto): Estimating Childlessness by Age and Race in the United States using a Bayesian Growth Curve Model Leontine Alkema (University of Massachussetts): A Bayesian approach to modeling demographic transitions with application to subnational estimation and forecasting of family planning and fertility indicators Douglas Leasure (Oxford): Population nowcasting in a digital world to support humanitarian action and sustainable development

Radu Craiu (Toronto): Bayesian Copula-based Latent Variable Models Daniel Heck (Marburg): Bayesian Modeling of Uncertainty in Stepwise Estimation Approaches Riccardo Rastelli (University College Dublin): A latent space model for multivariate time series analysis

Daniele Durante (Bocconi): Bayesian modeling of criminal networks Nial Friel (University College Dublin): Bayesian stochastic ordered block models Maarten Marsman (Amsterdam): Bayesian Edge Selection for Psychometric Network (Graphical) Models

Marco Corneli (Université Côté d’Azur): A Bayesian approach for clustering and exact finite-sample model selection in longitudinal data mixtures Irene Klugkist (Utrecht): Bayesian Evidence Synthesis in the context of informative hypotheses Eric-Jan Wagenmakers (Amsterdam): Optional Stopping

Herbert Hoijtink (Utrecht): Bayesian evaluation of single case experimental designs Adrian Raftery (University of Washington): Bayesian climate change assessment Robin Ryder (Paris-Dauphine and Imperial College London): Can Bayesian methods reconstruct deep language history?

François Caron (Oxford): Sparse Spatial Network Models for the analysis of mobility data Geoff Nicholls (Oxford): Partial order models for social hierarchies and rank-order data Amandine Véber (Université Paris Cité): Modelling expanding biological networks

Lots of great stuff here! I could do without the Bayes factor, but everything else looks really cool, an interesting mix of theoretical and applied topics.