The Infinite Latent Events Model
Machine Learning
2012-05-14 v1 Machine Learning
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
@article{arxiv.1205.2604,
title = {The Infinite Latent Events Model},
author = {David Wingate and Noah Goodman and Daniel Roy and Joshua Tenenbaum},
journal= {arXiv preprint arXiv:1205.2604},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)