F
Fredrik Ronquist
Researcher at Swedish Museum of Natural History
Publications - 128
Citations - 84851
Fredrik Ronquist is an academic researcher from Swedish Museum of Natural History. The author has contributed to research in topics: Monophyly & Markov chain Monte Carlo. The author has an hindex of 54, co-authored 122 publications receiving 76188 citations. Previous affiliations of Fredrik Ronquist include Uppsala University & Florida State University.
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Bayesian Analysis of Partitioned Data
TL;DR: Estimation under the Dirichlet process prior model discovers novel process partitions that may more effectively balance error variance and estimation bias, while rendering phylogenetic inference more robust to process heterogeneity by virtue of integrating estimates over all possible partition schemes.
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Probabilistic Graphical Model Representation in Phylogenetics
Sebastian Höhna,Sebastian Höhna,Tracy A. Heath,Tracy A. Heath,Bastien Boussau,Bastien Boussau,Michael J. Landis,Fredrik Ronquist,John P. Huelsenbeck,John P. Huelsenbeck +9 more
TL;DR: In this paper, the authors introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree and introduce modules to simplify the representation of standard components in large and complex models.
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The effect of ethanol concentration on the morphological and molecular preservation of insects for biodiversity studies
TL;DR: In this paper, the authors found that high ethanol concentrations indeed induce brittleness in insects, but the magnitude and nature of the effect varied strikingly among species and the number of appendages (legs, wings, antennae, heads) that had lost.
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Universal probabilistic programming offers a powerful approach to statistical phylogenetics
Fredrik Ronquist,Jan Kudlicka,Viktor Senderov,Johannes Borgström,Nicolas Lartillot,Daniel Lundén,Lawrence Murray,Thomas B. Schön,David Broman +8 more
TL;DR: In this article, the authors show that universal probabilistic programming languages (PPLs) solve the expressivity problem of phylogenetic analysis, while still supporting automated generation of efficient inference algorithms.