6 citations · 9 across the 9 of their papers we have counts for
9 papers
Decision-Theoretic Robustness for Network Models
Marios Papamichalis, Regina Ruane, Simon Lunagomez +1
Bayesian network models (Erdos Renyi, stochastic block models, random dot product graphs, graphons) are widely used in neuroscience, epidemiology, and the social sciences, yet real…
Wrapped Distributions on homogeneous Riemannian manifolds
Fernando Galaz-Garcia, Marios Papamichalis, Kathryn Turnbull +2
We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries. Control over distributio…
Bayesian modelling and computation utilising directed cycles in multiple network data
Anastasia Mantziou, Sally Keith, David M. P. Jacoby +2
Modelling multiple network data is crucial for addressing a wide range of applied research questions. However, there are many challenges, both theoretical and computational, to add…
Latent Space Network Modelling with Hyperbolic and Spherical Geometries
Marios Papamichalis, Kathryn Turnbull, Simon Lunagomez +1
A rich class of network models associate each node with a low-dimensional latent coordinate that controls the propensity for connections to form. Models of this type are well estab…
Bayesian model-based clustering for populations of network data
Anastasia Mantziou, Simon Lunagomez, Robin Mitra
There is increasing appetite for analysing populations of network data due to the fast-growing body of applications demanding such methods. While methods exist to provide readily i…
Robustness on Networks
Marios Papamichalis, Simon Lunagomez, Patrick J. Wolfe
We adopt the statistical framework on robustness proposed by Watson and Holmes in 2016 and then tackle the practical challenges that hinder its applicability to network models. The…
Lasso for hierarchical polynomial models
Hugo Maruri-Aguilar, Simon Lunagomez
In a polynomial regression model, the divisibility conditions implicit in polynomial hierarchy give way to a natural construction of constraints for the model parameters. We use th…
Evaluating and Optimizing Network Sampling Designs: Decision Theory and Information Theory Perspectives
Simón Lunagómez, Marios Papamichalis, Patrick J. Wolfe +1
Some of the most used sampling mechanisms that implicitly leverage a social network depend on tuning parameters; for instance, Respondent-Driven Sampling (RDS) is specified by the…
Bayesian Inference from Non-Ignorable Network Sampling Designs
Simon Lunagomez, Edoardo Airoldi
Consider a population of individuals and a network that encodes social connections among them. We are interested in making inference on finite population and super-population estim…