Scalable algorithms for bayesian inference of large-scale models from large-scale data

Omar Ghattas, Tobin Isaac, Noémi Petra, Georg Stadler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish (US)
Title of host publicationHigh Performance Computing for Computational Science
Subtitle of host publicationVECPAR 2016 - 12th International Conference, Revised Selected Papers
PublisherSpringer Verlag
Pages3-6
Number of pages4
Volume10150 LNCS
ISBN (Print)9783319619811
DOIs
StatePublished - 2017
Event12th International Conference on High Performance Computing for Computational Science, VECPAR 2016 - Porto, Portugal
Duration: Jun 28 2016Jun 30 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10150 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on High Performance Computing for Computational Science, VECPAR 2016
CountryPortugal
CityPorto
Period6/28/166/30/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ghattas, O., Isaac, T., Petra, N., & Stadler, G. (2017). Scalable algorithms for bayesian inference of large-scale models from large-scale data. In High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers (Vol. 10150 LNCS, pp. 3-6). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10150 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61982-8_1

Scalable algorithms for bayesian inference of large-scale models from large-scale data. / Ghattas, Omar; Isaac, Tobin; Petra, Noémi; Stadler, Georg.

High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. Vol. 10150 LNCS Springer Verlag, 2017. p. 3-6 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10150 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ghattas, O, Isaac, T, Petra, N & Stadler, G 2017, Scalable algorithms for bayesian inference of large-scale models from large-scale data. in High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. vol. 10150 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10150 LNCS, Springer Verlag, pp. 3-6, 12th International Conference on High Performance Computing for Computational Science, VECPAR 2016, Porto, Portugal, 6/28/16. https://doi.org/10.1007/978-3-319-61982-8_1
Ghattas O, Isaac T, Petra N, Stadler G. Scalable algorithms for bayesian inference of large-scale models from large-scale data. In High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. Vol. 10150 LNCS. Springer Verlag. 2017. p. 3-6. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-61982-8_1
Ghattas, Omar ; Isaac, Tobin ; Petra, Noémi ; Stadler, Georg. / Scalable algorithms for bayesian inference of large-scale models from large-scale data. High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. Vol. 10150 LNCS Springer Verlag, 2017. pp. 3-6 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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