2012/05/09 by David Wingate, Noah D. Goodman, Wingate, David +5
Computer Science · #Bayesian Methods and Mixture Models #Time Series Analysis and Forecasting #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.1205.2604
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.