We show that the proposed higher-order ergodicity coefficients provide new explicit formulas that (a) guarantee the uniqueness of Perron Z-eigenvectors of stochastic tensors, (b) provide bounds on the sensitivity of such eigenvectors with respect to changes in the tensor, and (c) ensure the convergence of different ...
Jul 10, 2019 · Ergodicity coefficients for stochastic matrices provide a valuable and widely used tool to analyze the long-term behavior of standard, first- ...
Aug 31, 2020 · Ergodicity coefficients for stochastic matrices provide a valuable and widely used tool to analyze the long-term behavior of standard, first- ...
The proposed higher-order ergodicity coefficients provide new explicit formulas that guarantee the uniqueness of Perron $Z$-eigenvectors of stochastic ...
The use of higher-order stochastic processes such as nonlinear Markov chains or vertex-reinforced random walks is significantly growing in recent years as ...
Ergodicity coefficients for stochastic matrices provide a valuable and widely used tool to analyze the long-term behavior of standard, first-order, Markov ...
They are used in [16] to define ergodicity coefficients for stochastic tensors and in [19] to define variational characterizations of some tensor eigenvalues ...
Jul 1, 2013 · have consequences more or less in the form that you want. For example that if there is a unique measure of maximal entropy then it is ergodic.
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Ergodicity coefficients for stochastic matrices determine inclusion regions for subdom- inant eigenvalues; estimate the sensitivity of the stationary ...
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Nov 7, 2019 · We introduce a set of ergodicity coefficients for stochastic cubical tensors, by extending certain definitions known in the matrix case to the ...