The Infinite Latent Events Model
Author(s)
Wingate, David; Goodman, Noah D.; Roy, Daniel; Tenenbaum, Joshua B.
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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.
Date issued
2009-06Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Laboratory for Information and Decision SystemsJournal
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