333 citations · 349 across the 20 of their papers we have counts for
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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…
What is your Prior Worth? Effective Sample Size and Sample Size Planning for Gaussian Graphical Models
Giuseppe Arena, Lourens Waldorp, Maarten Marsman
In Bayesian analysis, the prior effective sample size (ESS) expresses the information carried by a prior distribution in units of observations, quantifying how much independent inf…
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…
Efficient Bayes Factor Sensitivity Analysis via Posterior Density Ratios
František Bartoš, Eric-Jan Wagenmakers, Maarten Marsman +1
Bayes factor sensitivity analysis examines how the evidence for one hypothesis over another depends on the prior distribution. In complex models, the standard approach refits the m…
Blume-Capel model: Estimation of a three stable state network for , and data
Lourens Waldorp, Jonas Dalege, Maarten Marsman +3
An extension of the Ising model is proposed as a viable alternative for data with values , and in the inverse problem, i.e., estimation of the parameters. This model i…
Bayesian Inference for Discrete Markov Random Fields Through Coordinate Rescaling
Giuseppe Arena, Maarten Marsman
Discrete Markov random fields are undirected graphical models that capture complex conditional dependencies between discrete variables. Conducting exact posterior inference in thes…