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

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 7, Issue 4, 2010, Pages 763-767

  Miklós, István ^a   Mélykúti, Bence ^b   Swenson, Krister ^c  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c EPFL   (Switzerland)

Author keywords

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

Indexed keywords

BAYESIAN APPROACHES; BIOLOGY AND GENETICS.; GENOME REARRANGEMENT SCENARIOS; IMPORTANCE SAMPLING; MARKOV CHAIN; MARKOV CHAIN MONTE CARLO; POSTERIOR DISTRIBUTIONS; PROBLEM COMPLEXITY; RATE OF CONVERGENCE; SIGNED PERMUTATIONS;

APPROXIMATION THEORY; BAYESIAN NETWORKS; CHROMOSOMES; COMBINATORIAL MATHEMATICS; MONTE CARLO METHODS; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS;

MARKOV PROCESSES;

ARTICLE; BIOLOGY; GENE REARRANGEMENT; GENETICS; GENOME; METHODOLOGY; MONTE CARLO METHOD; PROBABILITY;

COMPUTATIONAL BIOLOGY; GENE REARRANGEMENT; GENOME; MARKOV CHAINS; MONTE CARLO METHOD;

EID: 78149256630     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2009.26     Document Type: Article

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