ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2021
Massive parallelization boosts big Bayesian multidimensional scaling.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Sparse Bayesian multidimensional scaling(s).Computational statistics · 2026Article
- Comparative performance of viral landscape phylogeography approaches.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Zigzag path connects two Monte Carlo samplers: Hamiltonian counterpart to a piecewise deterministic Markov process.Journal of the American Statistical Association · 2025Article
- Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies.Transactions of mathematics and its applications : a journal of the IMA · 2024Article
- Many-core algorithms for high-dimensional gradients on phylogenetic trees.Bioinformatics (Oxford, England) · 2024Article
- On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Massive Parallelization of Massive Sample-size Survival Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Genetic Diversity and Population Dynamics ofVeterinary medicine international · 2024Article
- First report of Strongylidae nematode from pilot whale (Frontiers in veterinary science · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
- A quantum parallel Markov chain Monte Carlo.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- From viral evolution to spatial contagion: a biologically modulated Hawkes model.Bioinformatics (Oxford, England) · 2022Article
- BAYESIAN MITIGATION OF SPATIAL COARSENING FOR A HAWKES MODEL APPLIED TO GUNFIRE, WILDFIRE AND VIRAL CONTAGION.The annals of applied statistics · 2022Article
- Relax, Keep Walking - A Practical Guide to Continuous Phylogeographic Inference with BEAST.Molecular biology and evolution · 2021Article
- Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.Statistics and computing · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through space-time, but its computational burden prevents its wider use. Crucial MDS model calculations scale quadratically in the number of observations. We partially mitigate this limitation through massive parallelization using multi-core central processing units, instruction-level vectorization and graphics processing units (GPUs). Fitting the MDS model using Hamiltonian Monte Carlo, GPUs can deliver more than 100-fold speedups over serial calculations and thus extend Bayesian MDS to a big data setting. To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe. We examine 5392 viral sequences and their associated 14 million pairwise distances arising from the number of commercial airline seats per year between viral sampling locations. To adjust for shared evolutionary history of the viruses, we implement a phylogenetic extension to the MDS model and learn that subtype H3N2 spreads most effectively, consistent with its epidemic success relative to other seasonal influenza subtypes. Finally, we provide MassiveMDS, an open-source, stand-alone C++ library and rudimentary R package, and discuss program design and high-level implementation with an emphasis on important aspects of computing architecture that become relevant at scale.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.