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stat.ME2022
Beyond Conjugacy for Chain Event Graph Model Selection
Aditi Shenvi, Silvia Liverani
Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian n…
stat.ME2022
Structure induced by a multiple membership transformation on the Conditional Autoregressive model
Marco Gramatica, Silvia Liverani, Peter Congdon
The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when…
stat.ME2022★ 2 cited
Variance matrix priors for Dirichlet process mixture models with Gaussian kernels
Wei Jing, Michail Papathomas, Silvia Liverani
The Dirichlet Process Mixture Model (DPMM) is a Bayesian non-parametric approach widely used for density estimation and clustering. In this manuscript, we study the choice of prior…