The Infinite Latent Events Model

David Wingate; Noah Goodman; Daniel Roy; Joshua Tenenbaum

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arXiv · 1205.2604

The Infinite Latent Events Model

David Wingate·Noah Goodman·Daniel Roy·Joshua Tenenbaum

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Abstract

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.

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David Wingate, Noah Goodman, Daniel Roy, Joshua Tenenbaum. 2012-05-09. The Infinite Latent Events Model. https://arxiv.org/abs/1205.2604

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