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Christoph Leuenberger

Researcher at University of Fribourg

Publications -  35
Citations -  2122

Christoph Leuenberger is an academic researcher from University of Fribourg. The author has contributed to research in topics: Population & Inference. The author has an hindex of 17, co-authored 35 publications receiving 1876 citations. Previous affiliations of Christoph Leuenberger include University of Bern & École Normale Supérieure.

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Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

TL;DR: The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment, which will realize substantial computational advances over standard ABC.
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Early farmers from across Europe directly descended from Neolithic Aegeans

TL;DR: This study demonstrates a direct genetic link between Mediterranean and Central European early farmers and those of Greece and Anatolia, extending the European Neolithic migratory chain all the way back to southwestern Asia.
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ABCtoolbox: a versatile toolkit for approximate Bayesian computations

TL;DR: ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.
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Bayesian Computation and Model Selection Without Likelihoods

TL;DR: This work proposes a reformulation of the regression adjustment of population subdivision among western chimpanzees in terms of a general linear model (GLM), which allows the integration into the sound theoretical framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors.
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Analysis of Ratios in Multivariate Morphometry

TL;DR: This work develops statistical procedures for the analysis of body ratios in a consistent multivariate statistical framework and presents a statistical derivation of the allometric size vector using the method of least squares.