Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2015

Abstract

Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP) style algorithm for collective graphical models. Mathematically, the algorithm is a strict generalization of BP—it can be viewed as an extension to minimize the Bethe free energy plus additional energy terms that are non-linear functions of the marginals. For CGMs, the algorithm is much more efficient than previous approaches to inference. We demonstrate its performance on two synthetic experiments concerning bird migration and collective human mobility.

Keywords

Artificial intelligence, Free energy, Functions, Graphic methods, Inference engines, Learning systems, Population statistics

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015, July 6-11

First Page

777

Last Page

786

ISBN

9781510810587

Publisher

JMLR

City or Country

Cambridge, MA

Copyright Owner and License

Authors

Additional URL

https://jmlr.org/proceedings/papers/v37/sunc15.pdf

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