2012
Collective Graphical Models
- Authors:
- Sheldon, D., and T.G. Dietterich
- Date:
- 2012
- Publication:
- Neural Information Processing Systems (NIPS), Grenada, Spain 12-15 December 2011
- 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.
- Download:
- Collective-Graphical-Models.pdf
- Online:
- http://people.cs.umass.edu/~sheldon/papers/cgm-single-file.pdf
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