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On an SVD-based algorithm for identifying meta-stable states of Markov chains. (English) Zbl 1321.65056
Summary: A Markov chain is a sequence of random variables $$X=\{x_t\}$$ that take on values in a state space $$\mathcal S$$.
A meta-stable state with respect to $$X$$ is a collection of states $$\mathcal E\subseteq\mathcal S$$ such that transitions of the form $$x_t\in\mathcal E$$ and $$x_{t+1}\notin\mathcal E$$ are exceedingly rare. In [D. Fritzsche et al., ETNA, Electron. Trans. Numer. Anal. 29, 46–69 (2007; Zbl 1171.15008)], an algorithm is presented that attempts to construct the meta-stable states of a given Markov chain. We supplement the discussion contained therein concerning the two main results.

##### MSC:
 65F15 Numerical computation of eigenvalues and eigenvectors of matrices 15A18 Eigenvalues, singular values, and eigenvectors 15B51 Stochastic matrices 60J10 Markov chains (discrete-time Markov processes on discrete state spaces) 60J20 Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.)
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