The Infinite Latent Events Model [article]

David Wingate, Noah Goodman, Daniel Roy, Joshua Tenenbaum
2012 arXiv   pre-print
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.
arXiv:1205.2604v1 fatcat:hgbmnhhmwrdtnf2izntnbfztgy