2011
NeurIPS
NeurIPS 2011
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Hot Topic Early Bird
- posterior distribution
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Mathematics & Optimization > Mathematics > Graph Theory
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo