9 citations · 13 across the 19 of their papers we have counts for
12 papers · 1 filter
Bayesian Causal Effect Estimation for Categorical Data using Staged Tree Models
Andrea Cremaschi, Manuele Leonelli, Gherardo Varando
We propose a fully Bayesian approach for causal inference with multivariate categorical data based on staged tree models, a class of probabilistic graphical models capable of repre…
Latent Modularity in Multi-View Data
Andrea Cremaschi, Maria De Iorio, Garritt Page +1
In this article, we consider the problem of clustering multi-view data, that is, information associated to individuals that form heterogeneous data sources (the views). We adopt a…
Repulsive mixtures via the sparsity-inducing partition prior
Alexander Mozdzen, Timothy Wertz, Maria De Iorio +3
We introduce a novel prior distribution for modelling the weights in mixture models based on a generalisation of the Dirichlet distribution, the Selberg Dirichlet distribution. Thi…
A Bayesian semi-parametric model for longitudinal growth and appetite phenotypes in children
Andrea Cremaschi, Beatrice Franzolini, Maria De Iorio +8
This study develops a Bayesian semi-parametric model to examine the longitudinal growth and appetite phenotypes in children from the GUSTO cohort, with a focus on understanding the…
A Bayesian time-varying random partition model for large spatio-temporal datasets
Giulio Beltramin, Andrea Cremaschi, Annalisa Cadonna +2
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated, according to a specific neighbouring structure. Motivated by a dataset on mobi…
Graph of Graphs: From Nodes to Supernodes in Graphical Models
Maria De Iorio, Willem van den Boom, Alexandros Beskos +2
High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodo…
Repulsion, Chaos and Equilibrium in Mixture Models
Andrea Cremaschi, Timothy M. Wertz, Maria De Iorio
Mixture models are commonly used in applications with heterogeneity and overdispersion in the population, as they allow the identification of subpopulations. In the Bayesian framew…
Bayesian Dynamic Network Modelling: an application to metabolic associations in cardiovascular diseases
Marco Molinari, Andrea Cremaschi, Maria De Iorio +3
We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients.…
Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment
Alexander Mozdzen, Andrea Cremaschi, Annalisa Cadonna +2
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated according to a specific neighboring structure. Incorporating the temporal and s…
Bayesian clustering of multiple zero-inflated outcomes
Beatrice Franzolini, Andrea Cremaschi, Willem van den Boom +1
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the Hurdle model, which explicitly models the pro…
Joint modelling of association networks and longitudinal biomarkers: an application to child obesity
Andrea Cremaschi, Maria De Iorio, Narasimhan Kothandaraman +3
The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importanc…
Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach
Andrea Cremaschi, Raffele Argiento, Maria De Iorio +4
Many applications in medical statistics as well as in other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals…