17 citations · 87 across the 28 of their papers we have counts for
5 papers · 1 filter
Bayes Factor Testing of Linear and Nonlinear Effects in Additive Gaussian Process Regression
Yueheng Wu, Joran Jongerling, Joris Mulder
Gaussian process regression flexibly models nonlinear relationships between predictors and an outcome variable. A central question in this modeling framework is whether each predic…
Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM
Joris Mulder
Vogels et al. (2024) presented an empirical comparison of Bayesian methods for structure learning in undirected Gaussian graphical models. The method implemented in the R package B…
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
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…