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The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths
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The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths

Istvan Miklos, Bence Melykuti et Krister Swenson
IEEE/ACM transactions on computational biology and bioinformatics, Vol.7(4), pp.763-767
01/10/2010
PMID: 21030742

Résumé

analysis of algorithms and problem complexity Bayesian methods Bioinformatics biology and genetics Convergence Genetic mutations Genomics Legged locomotion Markov processes Monte Carlo methods Polynomials Sampling methods Sorting Stochastic programming
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.

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