The Infinite Latent Events Model
David Wingate, Noah Goodman, Daniel Roy, Joshua Tenenbaum
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.
PDF Link: /papers/09/p607-wingate.pdf
AUTHOR = "David Wingate
and Noah Goodman and Daniel Roy and Joshua Tenenbaum",
TITLE = "The Infinite Latent Events Model",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "607--614"