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DOI: 10.5445/IR/1000054328

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

Aberer, Andre Jakob

Abstract:
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.


Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Hochschulschrift
Jahr 2015
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-543288
KITopen ID: 1000054328
Verlag 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
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