3 papers
stat.ME2026
A latent space network model for dynamic neural latent embedding
Riccardo Rastelli, Shizhe Chen
We introduce a novel latent space network model for analyzing multivariate time series of neural spike-train data. The methodology is motivated by an experimental study in mice, wh…
stat.ME2026
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…
stat.ME2026
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…