Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?

  title={Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?},
  author={J. Ruebeck and Ryan G. James and John R. Mahoney and James P. Crutchfield},
  volume={28 1},
Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by presenting the minimal generator of an arbitrary binary Markov process. This is a class of processes whose predictive model is well known. Surprisingly, the generative model requires three distinct topologies for different regions of parameter space. We show that a previously proposed generator for a particular set of… 

Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes

Among the predictive hidden Markov models that describe a given stochastic process, the {\epsilon}-machine is strongly minimal in that it minimizes every Renyi-based memory measure, but there are those for which there does not exist any strongly minimal model.

The fundamental thermodynamic bounds on finite models.

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The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

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Oracular information and the second law of thermodynamics

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Menggunakan Finite State Automata

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Minimum memory for generating rare events.

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Between Order and Chaos

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Exact common information

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A new dual to the Gács-Körner common information defined via the Gray-Wyner system

  • Sudeep KamathV. Anantharam
  • Computer Science
    2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2010
A new notion of common information between the random variables is proposed that is dual to the Gács-Körner common information from this viewpoint in a well-defined sense and characterized explicitly in terms of two auxiliary quantities that are asymmetric in nature.