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Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data

Statistics and Computing(2021)10.1007/s11222-020-09980-4Source: DataRank Database

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data is a research paper published in Statistics and Computing (2021). On theSindex it has a DataRank of 0.

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Authors (4)

Charles Loeffler ORCID,Seth Flaxman ORCID,Marc A. Suchard ORCID,Andrew J. Holbrook ORCID

Live enrichment

PubMed:34354329 PMC:PMC8330599

National Science Foundation

Grant: DMS1264153

National Institutes of Health

Grant: U19 AI135995

National Institutes of Health

Grant: K25AI153816

Engineering and Physical Sciences Research Council

Grant: EP/V002910/2

Engineering and Physical Sciences Research Council

Grant: EP/V002910/1

greenLicense: https://www.springernature.com/gp/researchers/text-and-data-mining

repositoryhttps://arxiv.org/pdf/2005.10123

publisherhttps://link.springer.com/content/pdf/10.1007/s11222-020-09980-4.pdf

publisherhttps://link.springer.com/article/10.1007/s11222-020-09980-4/fulltext.html

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8330599

repositoryhttps://arxiv.org/pdf/2005.10123.pdf

Keywords

GpuSimdMassive ParallelizationSpatiotemporal Hawkes Process

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data

FAIR Checklist

Authors (4)

Funding (5 grants)

Open Access

Topics

Annotations (Europe PMC)