5 citations · 14 across the 15 of their papers we have counts for
12 papers · 1 filter
Bayesian Conway-Maxwell-Poisson model with spike-and slab priors for dispersed count data with application to football scores
Nick Zhang, Riccardo Rastelli, Nial Friel
Statistical modeling for goals scored in football is typically achieved using the Poisson distribution and its variants. Here we propose a Bayesian framework for modeling under- an…
A Zero-Inflated Poisson Latent Position Cluster Model
Chaoyi Lu, Riccardo Rastelli, Nial Friel
The latent position network model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positi…
Latent Position Network Models
Hardeep Kaur, Riccardo Rastelli, Nial Friel +1
In this chapter, we present a review of latent position models for networks. We review the recent literature in this area and illustrate the basic aspects and properties of this mo…
GLAMLE: inference for multiview network data in the presence of latent variables, with application to commodities trading
Chaonan Jiang, Davide La Vecchia, Riccardo Rastelli
The statistical analysis of import/export data is helpful to understand the mechanism that determines exchanges in an economic network. The probability of having a commercial relat…
Continuous Latent Position Models for Instantaneous Interactions
Riccardo Rastelli, Marco Corneli
We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays, and…
A dynamic stochastic blockmodel for interaction lengths
Riccardo Rastelli, Michael Fop
We propose a new dynamic stochastic blockmodel that focuses on the analysis of interaction lengths in networks. The model does not rely on a discretization of the time dimension an…
The Sparse Latent Position Model for nonnegative weighted networks
Riccardo Rastelli
This paper introduces a new methodology to analyse bipartite and unipartite networks with nonnegative edge values. The proposed approach combines and adapts a number of ideas from…
Discussion of the article "Bayesian cluster analysis: point estimation and credible balls" by Wade and Ghahramani
Nial Friel, Riccardo Rastelli
We present a discussion of the paper "Bayesian cluster analysis: point estimation and credible balls" by Wade and Ghahramani. We believe that this paper contributes substantially t…
Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks
Riccardo Rastelli
The latent stochastic block model is a flexible and widely used statistical model for the analysis of network data. Extensions of this model to a dynamic context often fail to capt…
Choosing the number of groups in a latent stochastic block model for dynamic networks
Riccardo Rastelli, Pierre Latouche, Nial Friel
Latent stochastic block models are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to te…
Optimal Bayesian estimators for latent variable cluster models
Riccardo Rastelli, Nial Friel
In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share sim…
Properties of Latent Variable Network Models
Riccardo Rastelli, Nial Friel, Adrian E. Raftery
We derive properties of Latent Variable Models for networks, a broad class of models that includes the widely-used Latent Position Models. These include the average degree distribu…