8 papers
Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Jan I. Failenschmid, Leonie V. D. E. Vogelsmeier, Joris Mulder +1
Understanding dynamic systems is a central goal in many scientific disciplines. State-space models provide a general framework for studying latent dynamic systems based on indirect…
Modelling Interaction Duration in Relational Event Models
Rumana Lakdawala, Roger Leenders, Peter Ejbye-Ernst +1
The study of relational events, which are interactions occurring between actors over time, has gained significant traction recently. Traditional relational event models typically f…
Bayes Factor Hypothesis Testing in Meta-Analyses: Practical Advantages and Methodological Considerations
Joris Mulder, Robbie C. M. van Aert
Bayesian hypothesis testing via Bayes factors offers a principled alternative to classical p-value methods in meta-analysis, particularly suited to its cumulative and sequential na…
Invited Discussion of "Model Uncertainty and Missing Data: An Objective Bayesian Perspective" by Gonzalo GarcÃa-Donato , MarÃa Eugenia Castellanos , Stefano Cabras Alicia Quirós , and Anabel Forte
Merlise A Clyde
The article by Garc{Ã}a-Donato and co-authors addresses the dual challenges of accounting for model uncertainty and missing data within the Gaussian regression frameworks from an…
Comment on GarcÃa-Donato et al. (2025) "Model uncertainty and missing data: An objective Bayesian perspective"
Joris Mulder
Garcia-Donato et al. (2025) present a methodology for handling missing data in a model selection problem using an objective Bayesian approach. The current comment discusses an alte…
To Vary or Not To Vary: A Flexible Empirical Bayes Factor for Testing Variance Components
Fabio Vieira, Hongwei Zhao, Joris Mulder
Random effects are the gold standard for capturing structural heterogeneity in data, such as spatial dependencies, individual differences, or temporal dependencies. However, testin…