  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.
