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Issue No.04 - October-December (2010 vol.7)

pp: 763-767

Bence Mélykúti , University of Oxford, Oxford

István Miklós , Renyi Institute of Mathematical Sciences , Budapest

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.26

ABSTRACT

Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. We prove that the relaxation time of the Markov chains walking on the optimal reversal sorting scenarios might grow exponentially with the size of the signed permutations, namely, with the number of syntheny blocks.

INDEX TERMS

Stochastic programming, Markov processes, analysis of algorithms and problem complexity, biology and genetics.

CITATION

Bence Mélykúti, István Miklós, "The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths",

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