The metropolized partial importance sampling MCMC mixes slowly on minimum reversal rearrangement paths

I. Miklós, Bence Mélykúti, Krister Swenson

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Article number4796188
Pages (from-to)763-767
Number of pages5
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume7
Issue number4
DOIs
Publication statusPublished - 2010

Fingerprint

Importance sampling
Markov Chains
Importance Sampling
Markov Chain Monte Carlo
Reversal
Rearrangement
Markov processes
Markov chain
Signed Permutations
Genome Rearrangement
Partial
Scenarios
Path
Posterior distribution
Bayesian Approach
Relaxation Time
Sorting
Rate of Convergence
Bayes Theorem
Relaxation time

Keywords

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

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics
  • Medicine(all)

Cite this

The metropolized partial importance sampling MCMC mixes slowly on minimum reversal rearrangement paths. / Miklós, I.; Mélykúti, Bence; Swenson, Krister.

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 7, No. 4, 4796188, 2010, p. 763-767.

Research output: Contribution to journalArticle

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