7 citations · 19 across the 7 of their papers we have counts for
4 papers · 1 filter
BEAGLE 4.1: A high-performance library for computation on phylogenetic trees across diverse parallel architectures
Karthik Gangavarapu, Xiang Ji, Yucai Shao +4
Efficient evaluation of sequence data likelihoods and their high-dimensional gradients on phylogenetic trees improves inference under both maximum-likelihood and Bayesian framework…
TreeFlow: probabilistic modelling and automatic differentiation for phylogenetics
Christiaan Swanepoel, Mathieu Fourment, Xiang Ji +4
Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular…
Automatic differentiation is no panacea for phylogenetic gradient computation
Mathieu Fourment, Christiaan J. Swanepoel, Jared G. Galloway +4
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily…
Scalable Bayesian divergence time estimation with ratio transformations
Xiang Ji, Alexander A. Fisher, Shuo Su +5
Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With t…