Massive Parallelization Boosts Big Bayesian Multidimensional Scaling

DOI10.1080/10618600.2020.1754226OpenAlexW3021948211MaRDI QIDQ5066411

Andrew Rambaut, Marc A. Suchard, Dirk Brockmann, Guy Baele, Philippe Lemey, Simon Dellicour, Andrew J. Holbrook

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218718

zbMATH Keywords

graphics processing unitHamiltonian Monte Carlomassive parallelizationBayesian phylogeographymultiple-datasingle-instruction

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion ⋮ A Quantum Parallel Markov Chain Monte Carlo ⋮ Vector operations for accelerating expensive Bayesian computations - a tutorial guide ⋮ MassiveMDS

Uses Software

• R
• Theano
• Rcpp
• BayesDA
• BEAGLE
• Python
• Intel TBB
• RcppParallel
• bmds
• RcppNT2

Cites Work

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• Unnamed Item
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