Collective Graphical Models

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

Bibtex Metadata Paper Supplemental

Authors

Daniel R. Sheldon, Thomas Dietterich

Abstract

There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregate information (counts or low-dimensional contingency tables). This paper introduces Collective Graphical Models---a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model. We derive a highly-efficient Gibbs sampling algorithm for sampling from the posterior distribution of the sufficient statistics conditioned on noisy aggregate observations, prove its correctness, and demonstrate its effectiveness experimentally.