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Exploring Predictive States via Cantor Embeddings and Wasserstein Distance
Predictive states for stochastic processes are a nonparametric and interpretable construct with relevance across a multitude of modeling paradigms.
Topology, Convergence, and Reconstruction of Predictive States
Predictive equivalence in discrete stochastic processes have been applied with great success to identify randomness and structure in statistical physics and chaotic dynamical systems and to inferring hidden Markov models.
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