Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields

Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios. Motivated by applications arising in the transportation domain, in this paper we develop a model for learning heterogeneous and dynamic patterns of velocity field data... (read more)

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Methods used in the Paper

Gaussian Process
Non-Parametric Classification