2 citations · 2 across the 2 of their papers we have counts for
6 papers
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…
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…
Bayesian modelling for spatially misaligned health areal data: a multiple membership approach
Marco Gramatica, Peter Congdon, Silvia Liverani
Diabetes prevalence is on the rise in the UK, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to…
Dirichlet Process Mixture Models for Regression Discontinuity Designs
Federico Ricciardi, Silvia Liverani, Gianluca Baio
The Regression Discontinuity Design (RDD) is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold value for a c…
Predicting success in the worldwide start-up network
Moreno Bonaventura, Valerio Ciotti, Pietro Panzarasa +3
By drawing on large-scale online data we construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent…
Modelling collinear and spatially correlated data
Silvia Liverani, Aurore Lavigne, Marta Blangiardo
In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in…