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Markov chain SUBST

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This means that the Markov chain is ergodic, so there is a unique stationary distribution which characterizes the long-run behavior of the process.
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If the Markov chain is irreducible and aperiodic, then there is a unique stationary distribution.
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Each of the states of the Markov chain represents one of the phases.
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A state is stochastically stable if it attracts positive weight in the stationary distribution of the Markov chain.
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This is the same continuous time Markov chain as in a birthdeath process.
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A Markov chain is constructed in such a way as to have the integrand as its equilibrium distribution.
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A good chain will have "rapid mixing" the stationary distribution is reached quickly starting from an arbitrary positiondescribed further under Markov chain mixing time.
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The username is a play on the words Markov chain.
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The Markov chain technique is one method which has been used to generate texts by algorithm and randomizing techniques that seem meaningful.
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An irreducible markov chain only needs one aperiodic state to imply all states are aperiodic.
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