The Infinite Latent Events Model
Name
Tenenbaum-The Infinite Latent Events Model.pdf
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360.25 KB
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Author(s)
Wingate, David
Goodman, Noah D.
Roy, Daniel
Tenenbaum, Joshua B.
Date Issued
June 2009
Journal
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence ( 2009 ) June 18- 21 2009, Montreal, QC, Canada
Publisher
Association for Uncertainty in Artificial Intelligence Press
Citation
Wingate, David, Noah Goodman, Daniel Roy and Joshua Tenenbaum. "The Infinite Latent Events Model." in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, June 18-21, 2009, Montreal, QC, Canada. p.607-614.
Version
Author's final manuscript
Abstract
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a set of latent events, which events fired at each timestep, and how those events are causally linked. We illustrate the model on a sound factorization task, a network topology identification task, and a video game task.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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