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. 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.

CRG Leaders: Raphael Gottardo (UBC), Paul Gustafson (UBC), Lurdes Inoue (UW), Adrian Raftery (UW) and Tim Swartz (SFU)Other faculty participating include: Derek Bingham (SFU), Bertrand Clarke (UBC), Nando De Freitas (UBC), Adrian Dobra(UW), Arnaud Doucet (UBC), Paramjit Gill (UBC-O), Peter Hoff (UW), Kevin Murphy (UBC), Dale Schuurmans (UoA), Cory Burtz (UoR).

Postdoc and students founded by PIMS: Francois Caron (PIMS-CRNS PDF, UBC), Kenneth Lo (UBC).

Visitors:
Francesca Dominici (Biostatistics, Harvard) April 2010
Giovanni Parmigiani (Biostatistics, Harvard) April 2010
Sylvia Richardson (Imperial College) October, 2009.
Radu Craiu (Statistics, University of Toronto) April 15-23, 2008.