The Infinite Latent Events Model
This work addresses the challenge of modeling complex causal relationships in timeseries data for researchers in machine learning and statistics, though it appears incremental as it builds on existing nonparametric Bayesian methods.
The authors tackled the problem of learning structure in discrete timeseries data by introducing the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution for infinite dimensional Dynamic Bayesian Networks, and demonstrated its application on tasks like sound factorization, network topology identification, and a video game task.
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.