On the Markov chain central limit theorem.
Jones, Galin L. (2004)
Probability Surveys [electronic only]
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Displaying similar documents to “Markov chain comparison.”
On the Markov chain central limit theorem.
On variance conditions for Markov chain CLTs.
Häggström, Olle, Rosenthal, Jeffrey S. (2007)
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Sufficient conditions for torpid mixing of parallel and simulated tempering.
Woodard, Dawn B., Schmidler, Scott C., Huber, Mark (2009)
Electronic Journal of Probability [electronic only]
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Bounding fastest mixing.
Roch, Sébastien (2005)
Electronic Communications in Probability [electronic only]
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Merging of states of Markov chains with infinite probability rates
Petr Kůrka (1979)
Commentationes Mathematicae Universitatis Carolinae
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A martingale proof of Dobrushin's theorem for non-homogeneous Markov chains.
Sethuraman, S., Varadhan, S.R.S. (2005)
Electronic Journal of Probability [electronic only]
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Mixing time bounds via the spectral profile.
Goel, Sharad, Montenegro, Ravi, Tetali, Prasad (2006)
Electronic Journal of Probability [electronic only]
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Geometric ergodicity and hybrid Markov chains.
Roberts, Gareth O., Rosenthal, Jeffrey S. (1997)
Electronic Communications in Probability [electronic only]
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The computation of stationary distributions of Markov chains through perturbations.
Hunter, Jeffrey J. (1991)
Journal of Applied Mathematics and Stochastic Analysis
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Covariance and relaxation time in finite Markov chains.
Keilson, Julian (1998)
Journal of Applied Mathematics and Stochastic Analysis
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Why the Kemeny Time is a constant
Karl Gustafson, Jeffrey J. Hunter (2016)
Special Matrices
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We present a new fundamental intuition forwhy the Kemeny feature of a Markov chain is a constant. This new perspective has interesting further implications.