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

Omar Ghattas; Tobin Isaac; Noémi Petra; Georg Stadler

doi:10.1007/978-3-319-61982-8_1

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

Omar Ghattas, Tobin Isaac, Noémi Petra, Georg Stadler

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English (US)
Title of host publication	High Performance Computing for Computational Science
Subtitle of host publication	VECPAR 2016 - 12th International Conference, Revised Selected Papers
Editors	Ines Dutra, Rui Camacho, Jorge Barbosa, Osni Marques
Publisher	Springer Verlag
Pages	3-6
Number of pages	4
ISBN (Print)	9783319619811
DOIs	https://doi.org/10.1007/978-3-319-61982-8_1
State	Published - 2017
Event	12th International Conference on High Performance Computing for Computational Science, VECPAR 2016 - Porto, Portugal Duration: Jun 28 2016 → Jun 30 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10150 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	12th International Conference on High Performance Computing for Computational Science, VECPAR 2016
Country/Territory	Portugal
City	Porto
Period	6/28/16 → 6/30/16

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-61982-8_1

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 I. Dutra, R. Camacho, J. Barbosa, & O. Marques (Eds.), High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers (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 et al.
High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. ed. / Ines Dutra; Rui Camacho; Jorge Barbosa; Osni Marques. 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 proceeding › Conference contribution

Ghattas, O, Isaac, T, Petra, N & Stadler, G 2017, Scalable algorithms for bayesian inference of large-scale models from large-scale data. in I Dutra, R Camacho, J Barbosa & O Marques (eds), High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. 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 Dutra I, Camacho R, Barbosa J, Marques O, editors, High Performance Computing for Computational Science: VECPAR 2016 - 12th International Conference, Revised Selected Papers. Springer Verlag. 2017. p. 3-6. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61982-8_1

Ghattas, Omar ; Isaac, Tobin ; Petra, Noémi et al. / 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. editor / Ines Dutra ; Rui Camacho ; Jorge Barbosa ; Osni Marques. 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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title = "Scalable algorithms for bayesian inference of large-scale models from large-scale data",

author = "Omar Ghattas and Tobin Isaac and No{\'e}mi Petra and Georg Stadler",

note = "Funding Information: This work was supported by AFOSR grants FA9550-12-1-0484 and FA9550-09-1-0608, DARPA/ARO contract W911NF-15-2-0121, DOE grants DE-SC0010518, DE-SC0009286, DE-11018096, DE-SC0006656, DE-SC0002710, and DE-FG02-08ER25860, and NSF grants ACI-1550593, CBET-1508713, CBET-1507009, CMMI-1028889, and ARC-0941678. Computations were performed on supercomputers at TACC, ORNL, and LLNL. We gratefully acknowledge this support. ; 12th International Conference on High Performance Computing for Computational Science, VECPAR 2016 ; Conference date: 28-06-2016 Through 30-06-2016",

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N1 - Funding Information: This work was supported by AFOSR grants FA9550-12-1-0484 and FA9550-09-1-0608, DARPA/ARO contract W911NF-15-2-0121, DOE grants DE-SC0010518, DE-SC0009286, DE-11018096, DE-SC0006656, DE-SC0002710, and DE-FG02-08ER25860, and NSF grants ACI-1550593, CBET-1508713, CBET-1507009, CMMI-1028889, and ARC-0941678. Computations were performed on supercomputers at TACC, ORNL, and LLNL. We gratefully acknowledge this support.

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ER -

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

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