AI Article Synopsis
- The context-tree weighting method is used to estimate topological entropy through a symbolic dynamics approach, minimizing the need for manual parameter selection.
- This method can infer both the system's transition structure and probabilities effectively.
- Real-world examples, like a Markov model and the Hénon map, show that this technique achieves accurate results quickly with short symbolic sequences.
Article Abstract
We estimate topological entropy via symbolic dynamics using a data compression technique called the context-tree weighting method. Unlike other symbolic dynamical approaches, which often have to choose ad hoc parameters such as the depth of a tree, the context-tree weighting method is almost parameter-free and infers the transition structure of the system as well as transition probabilities. Our examples, including a Markov model, the logistic map, and the Hénon map, demonstrate that the convergence is fast: one obtains the theoretically correct topological entropy with a relatively short symbolic sequence.
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| http://dx.doi.org/10.1103/PhysRevE.67.026205 | DOI Listing |
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