Titlebook: New Frontiers in Bayesian Statistics; BAYSM 2021, Online, Raffaele Argiento,Federico Camerlenghi,Sally Pagan Conference proceedings 2022 T

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,Power-Expected-Posterior Methodology with Baseline Shrinkage Priors,ective, in regression models. The PEP prior inherits all of the advantages of Expected-Posterior-Prior (EPP) and furthermore it drops the need of selection over the imaginary data and decreases their effect over the final prior. Under the PEP prior methodology an initial (usually default) baseline p

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,Bayesian Nonparametric Scalar-on-Image Regression via Potts-Gibbs Random Partition Models,esian nonparametric scalar-on-image regression model that utilises the spatial coordinates of the voxels to group voxels with similar effects on the response to have a common coefficient. We employ the Potts-Gibbs random partition model as the prior for the random partition in which the partition pr

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,Specification of the Base Measure of Nonparametric Priors via Random Means,e in Bayesian Nonparametrics: understanding the behavior of a finite dimensional feature of a flexible and infinite-dimensional prior is crucial for prior elicitation. In particular distributions of means of nonparametric priors have been the object of thorough investigation in the literature. We ta

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