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stat.ME2026
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…
stat.ME2026
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…