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Bayesian Multitask with Structure Learning

RESOURCE

Publicly Accessible Repository
https://github.com/LLNL/bmsl
https://doi.org/10.11578/dc.20201216.2

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Abstract

Software including a Bayesian multitask learning model. The pipeline includes functionality to process data, train, evaluate models, and generate reports.
Developers:
Soper, Braden [1] ;  Widemann, David [1] ;  Ray, Priyadip [1] ;  De Oliveira Sales, Ana Paula [1] ;  Goncalves, Andre [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Release Date:
2019-10-31
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Version:
1.0
Licenses:
MIT License
Sponsoring Org.:
Code ID:
49434
Site Accession Number:
LLNL-CODE- 810263
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/LLNL/bmsl
https://doi.org/10.11578/dc.20201216.2

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LLNL Badge

Citation Formats

Soper, Braden C., Widemann, David P., Ray, Priyadip, De Oliveira Sales, Ana Paula, and Goncalves, Andre R. Bayesian Multitask with Structure Learning. Computer Software. https://github.com/LLNL/bmsl. USDOE National Nuclear Security Administration (NNSA). 31 Oct. 2019. Web. doi:10.11578/dc.20201216.2.
Soper, Braden C., Widemann, David P., Ray, Priyadip, De Oliveira Sales, Ana Paula, & Goncalves, Andre R. (2019, October 31). Bayesian Multitask with Structure Learning. [Computer software]. https://github.com/LLNL/bmsl. https://doi.org/10.11578/dc.20201216.2.
Soper, Braden C., Widemann, David P., Ray, Priyadip, De Oliveira Sales, Ana Paula, and Goncalves, Andre R. "Bayesian Multitask with Structure Learning." Computer software. October 31, 2019. https://github.com/LLNL/bmsl. https://doi.org/10.11578/dc.20201216.2.
@misc{ doecode_49434,
title = {Bayesian Multitask with Structure Learning},
author = {Soper, Braden C. and Widemann, David P. and Ray, Priyadip and De Oliveira Sales, Ana Paula and Goncalves, Andre R.},
abstractNote = {Software including a Bayesian multitask learning model. The pipeline includes functionality to process data, train, evaluate models, and generate reports.},
doi = {10.11578/dc.20201216.2},
url = {https://doi.org/10.11578/dc.20201216.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20201216.2}},
year = {2019},
month = {oct}
}