10 citations · 21 across the 18 of their papers we have counts for
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stat.CO2021
Bayesian nonparametric model based clustering with intractable distributions: an ABC approach
Mario Beraha, Riccardo Corradin
Bayesian nonparametric mixture models offer a rich framework for model based clustering. We consider the situation where the kernel of the mixture is available only up to an intrac…
stat.CO2021★ 10 cited
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software
Mario Beraha, Daniele Falco, Alessandra Guglielmi
The aim of this work is the comparison of the performance of the three popular software platforms JAGS, NIMBLE and Stan. These probabilistic programming languages are able to autom…
stat.ME2021★ 5 cited
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha
We present a novel class of projected methods, to perform statistical analysis on a data set of probability distributions on the real line, with the 2-Wasserstein metric. We focus…