Collective Graphical Models
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.
Cite
Text
Sheldon and Dietterich. "Collective Graphical Models." Neural Information Processing Systems, 2011.Markdown
[Sheldon and Dietterich. "Collective Graphical Models." Neural Information Processing Systems, 2011.](https://mlanthology.org/neurips/2011/sheldon2011neurips-collective/)BibTeX
@inproceedings{sheldon2011neurips-collective,
title = {{Collective Graphical Models}},
author = {Sheldon, Daniel R. and Dietterich, Thomas G.},
booktitle = {Neural Information Processing Systems},
year = {2011},
pages = {1161-1169},
url = {https://mlanthology.org/neurips/2011/sheldon2011neurips-collective/}
}