Reversible Markov chains possess finite-time spectral rigidity controlled by eigenvalue separation, with two-sided bounds on rigidity time and a covariance-based spectral entropy theory.
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Graph theory yields explicit combinatorial formulas showing mutual linearity for transient occupation probabilities and hitting time distributions in Markov networks.
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Quantitative Spectral Rigidity and Finite-Time Spectral Thermodynamics in Reversible Markov Chains
Reversible Markov chains possess finite-time spectral rigidity controlled by eigenvalue separation, with two-sided bounds on rigidity time and a covariance-based spectral entropy theory.
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Graph theoretic derivation of mutual linearity for transient probabilities and hitting time distributions in Markov networks
Graph theory yields explicit combinatorial formulas showing mutual linearity for transient occupation probabilities and hitting time distributions in Markov networks.