RT Journal Article
JF IEEE/ACM Transactions on Computational Biology and Bioinformatics
YR 2010
VO 7
IS 4
SP 763
TI The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths
A1 Istvan Miklos,
A1 Bence Melykuti,
A1 Krister Swenson,
K1 Monte Carlo methods
K1 Bioinformatics
K1 Genomics
K1 Sorting
K1 Genetic mutations
K1 Polynomials
K1 Sampling methods
K1 Bayesian methods
K1 Convergence
K1 Legged locomotion
AB 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.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1545-5963
LA English
DO 10.1109/TCBB.2009.26
LK http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.26