Learning Temporal Dependence from Time-Series Data with Latent Variables

27 Aug 2016Hossein HosseiniSreeram KannanBaosen ZhangRadha Poovendran

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest and applicability is the setting where the noise is Gaussian and relationships are Markov and linear... (read more)

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