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Algorithmic Advancements and Massive Parallelism for Large-Scale Datasets in Phylogenetic Bayesian Markov Chain Monte Carlo

Aberer, Andre Jakob

Datasets used for the inference of the "tree of life" grow at unprecedented rates, thus inducing a high computational burden for analytic methods. First, we introduce a scalable software package that allows us to conduct state of the art Bayesian analyses on datasets of almost arbitrary size. Second, we derive a proposal mechanism for MCMC that is substantially more efficient than traditional branch length proposals. Third, we present an efficient algorithm for solving the rogue taxon problem.

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DOI: 10.5445/IR/1000054328
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Hochschulschrift
Jahr 2015
Sprache Englisch
Identifikator urn:nbn:de:swb:90-543288
KITopen-ID: 1000054328
Verlag KIT, Karlsruhe
Umfang XIII, 162 S.
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Theoretische Informatik (ITI)
Prüfungsdaten 07.12.2015
Referent/Betreuer Prof. A. Stamatakis
Schlagworte phylogenetics, parallelization, high-performance computing, Bayesian statistics
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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