Journal article

Pakman: a modular, efficient and portable tool for approximate Bayesian inference

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Pak, T. F., Baker, R. E., & Pitt-Francis, J. M. (2020). Pakman: a modular, efficient and portable tool for approximate Bayesian inference. Journal of Open Source Software, 5(47), 1716–1716.

MLA Style

Pak, T. F., et al. “Pakman: a Modular, Efficient and Portable Tool for Approximate Bayesian Inference.” Journal of Open Source Software, vol. 5, no. 47, Open Journals, 2020, pp. 1716–16.

Chicago Style

Pak, TF, RE Baker, and JM Pitt-Francis. 2020. “Pakman: a Modular, Efficient and Portable Tool for Approximate Bayesian Inference.” Journal of Open Source Software 5 (47): 1716–16.
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Files:: Pak_et_al_2020_Pakman_a_modular.pdf

(Preview, Version of record, pdf, 376.3KB, Terms of use)

Publisher copy:: 10.21105/joss.01716

Authors

+ Pak, TF More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Mathematical Institute
Role:: Author
ORCID:: 0000-0002-7198-7688

+ Baker, RE More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Mathematical Institute
Role:: Author
ORCID:: 0000-0002-6304-9333

+ Pitt-Francis, JM More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Computer Science
Role:: Author
ORCID:: 0000-0002-5094-5403

+ Biotechnology and Biological Sciences Research Council More from this funder

Funder identifier:: https://ror.org/00cwqg982
Grant:: BB/R000816/1

Publisher:: Open Journals
Journal:: Journal of Open Source Software More from this journal
Volume:: 5
Issue:: 47
Pages:: 1716-1716
Publication date:: 2020-03-07
Acceptance date:: 2020-03-07
DOI:: 10.21105/joss.01716
EISSN:: 2475-9066

Language:: English
Keywords:: MPI

approximate Bayesian computation

Bayesian inference

parallel computing

distributed computing
Pubs id:: 1092972
Local pid:: pubs:1092972
Deposit date:: 2020-03-12

Terms of use

Copyright holder:: Pak et al.
Copyright date:: 2020
Rights statement:: Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

Licence:: CC Attribution (CC BY)

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