Convergence of Markov Chains

Part of the Universitext book series (UTX)
We consider a Markov chain X with invariant distribution π and investigate conditions under which the distribution of X n converges to π for n → ∞. Essentially it is necessary and sufficient that the state space of the chain cannot be decomposed into subspaces
  • that the chain does not leave

  • or that are visited by the chain periodically; e.g., only for odd n or only for even n.


Markov Chain Random Walk Transition Matrix Ising Model Gibbs Sampler 
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Copyright information

© Springer-Verlag London Limited 2008

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