The Infinite Latent Events Model
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
Text
Wingate et al. "The Infinite Latent Events Model." Conference on Uncertainty in Artificial Intelligence, 2009.Markdown
[Wingate et al. "The Infinite Latent Events Model." Conference on Uncertainty in Artificial Intelligence, 2009.](https://mlanthology.org/uai/2009/wingate2009uai-infinite/)BibTeX
@inproceedings{wingate2009uai-infinite,
title = {{The Infinite Latent Events Model}},
author = {Wingate, David and Goodman, Noah D. and Roy, Daniel M. and Tenenbaum, Joshua B.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {607-614},
url = {https://mlanthology.org/uai/2009/wingate2009uai-infinite/}
}