The Infinite Latent Events Model

David Wingate, Noah Goodman, Daniel Roy, Josh Tenenbaum
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:607-614, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-wingate09a, title = {The Infinite Latent Events Model}, author = {Wingate, David and Goodman, Noah and Roy, Daniel and Tenenbaum, Josh}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {607--614}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/wingate09a/wingate09a.pdf}, url = {https://proceedings.mlr.press/r7/wingate09a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T The Infinite Latent Events Model %A David Wingate %A Noah Goodman %A Daniel Roy %A Josh Tenenbaum %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-wingate09a %I PMLR %P 607--614 %U https://proceedings.mlr.press/r7/wingate09a.html %V R7 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Wingate, D., Goodman, N., Roy, D. & Tenenbaum, J.. (2009). The Infinite Latent Events Model. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:607-614 Available from https://proceedings.mlr.press/r7/wingate09a.html. Reissued by PMLR on 04 October 2026.

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