5 citations · 5 across the 5 of their papers we have counts for
5 papers
Accelerating Bayesian Variable Selection using Piecewise Deterministic Markov Processes
Don van den Bergh, Maarten Marsman
Bayesian variable selection becomes computationally challenging when models contain many dependent parameters. We study Piecewise Deterministic Markov Process (PDMP) samplers as a…
Reversible Jump MCMC With No Regrets: Bayesian Variable Selection Using Mixtures of Mutually Singular Distributions
Don van den Bergh, Merlise A. Clyde, Adrian E. Raftery +1
Bayesian variable selection requires sampling from a posterior distribution that combines discrete model indicators with continuously varying parameters, a challenge often addresse…
Comparing Variable Selection and Model Averaging Methods for Logistic Regression
Nikola Sekulovski, František Bartoš, Don van den Bergh +6
Model uncertainty is a central challenge in statistical models for binary outcomes such as logistic regression, arising when it is unclear which predictors should be included in th…
Flexible Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors
Don van den Bergh, Fabian Dablander
Researchers frequently wish to assess the equality or inequality of groups, but this poses the challenge of adequately adjusting for multiple comparisons. Statistically, all possib…
Default Bayes Factors for Testing the (In)equality of Several Population Variances
Fabian Dablander, Don van den Bergh, Eric-Jan Wagenmakers +1
Testing the (in)equality of variances is an important problem in many statistical applications. We develop default Bayes factor tests to assess the (in)equality of two or more popu…