Bayesian Modeling and Computation for Networks

2008 2010


This PIMS funded collaborative research group focuses on Bayesian methods for network analysis, paying special attention to model design and computational issues of learning and inference. Bayesian inference is an approach to statistics in which all forms of uncertainty are expressed in terms of probability. Non-Bayesian approaches to inference have dominated statistical theory and practice for most of the past century, but the last two decades have seen a reemergence of Bayesian statistical inference. This is mainly due to the dramatic increase in computer power and the availability of new computational tools, including variational techniques, Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC). Bayesian modeling has become common practice as it provides a powerful method for coping with very complex stochastic domains, including networks. Networks are widely used to represent data on relations between interacting actors or nodes. Among many things, they can be used to describe social networks, genetic regulatory networks, computer networks, and sensor networks. In these settings, traditional independence assumptions are blatantly inappropriate; the structure of relationships between the data must be taken into account. As a result, there has been increasing research developing techniques for incorporating network structures into machine learning and statistics. This collaborative research group will bring together researchers working on Bayesian modeling for networks from different communities, thereby fostering collaborations and intellectual exchange. Our hope is that this will result in novel modeling approaches, diverse applications, and new research directions. In particular we will focus on three main problems: social networks, regulatory networks and sensor networks. Even though the three problems share many similar features, both in terms of modeling and computation, they are usually treated separately.



CRG Leaders

Participating Faculty

Post-Doc and Students funded by PIMS


  • Radu Craiu (Statistics, University of Toronto) April 15-23, 2008. Radu gave a talk on "Learn from Thy Neighbour: Parallel-Chain Adaptive MCMC" on April 15; click here for more details.
  • Francesca Dominici and Giovanni Parmigiani (Biostatistics, Harvard), April, 2010

Distinguished Visitor: Prof. Sylvia Richardson (Imperial College, London), UW, October, 2010