Mitchell Centre Seminar Series: Isabella Gollini
|15 October 2014
|16:00 - 17:30
|What is it:
|School of Social Sciences
Weekly seminar series of the Mitchell Centre for Social Network Analysis.
Isabella Gollini, University of Bristol
Bayesian framework for social network analysis with focus on latent variable network models
Recent research in statistical social network analysis has demonstrated the advantages and effectiveness of probabilistic approaches to social network data. The general Bayesian framework codifies how to proceed with the inference in a rational way by propagating information through Bayes theorem. In this talk we describe the main steps of the Bayesian framework for social network analysis: probabilistic modelling, parameter estimation, model selection, and predictive Bayesian goodness-of-fit testing. A particular focus will be given to latent variable network models and advanced inferential methods, which allow to efficiently describe the relational structure of large network data.
Role: Research Assistant in Bayesian Statistics
Organisation: University of Bristol
Travel and Contact Information
Humanities Bridgeford Street