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stat.ME2026
Marginal Data Augmentation for Efficient Bayesian Modeling of Counts and Rates with a Demographic Application
Gregor Zens, Sylvia Frühwirth-Schnatter
Count data models are ubiquitous in many fields, yet Bayesian data augmentation algorithms for such models frequently encounter challenges with Markov chain Monte Carlo efficiency.…
stat.ME2025
Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes
Gregor Zens, Mark F. J. Steel
We propose a fast and theoretically grounded method for Bayesian variable selection and model averaging in latent variable regression models. Our framework addresses three interrel…
stat.ME2025
Model Uncertainty in Latent Gaussian Models with Univariate Link Function
Mark F. J. Steel, Gregor Zens
We consider a class of latent Gaussian models with a univariate link function (ULLGMs). These are based on standard likelihood specifications (such as Poisson, Binomial, Bernoulli,…