7 citations · 9 across the 4 of their papers we have counts for
5 papers
BEAGLE in Action: Phylogenetic Likelihoods for Very Large State Spaces
Benjamin D Redelings, April Wright, Karthik Gangavarapu +9
Computing phylogenetic likelihoods becomes challenging when the continuous-time Markov chain has a large state space. At each branch, pruning algorithms require terms of the form $…
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…
Many-core algorithms for high-dimensional gradients on phylogenetic trees
Karthik Gangavarapu, Xiang Ji, Guy Baele +4
The rapid growth in genomic pathogen data spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters o…
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…
Branch: An interactive, web-based tool for testing hypotheses and developing predictive models
Karthik Gangavarapu, Vyshakh Babji, Tobias Meißner +2
Branch is a web application that provides users with no programming with the ability to interact directly with large biomedical datasets. The interaction is mediated through a coll…