Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Marko Järvenpää; Aki Vehtari; Pekka Marttinen

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Marko Järvenpää, Aki Vehtari, Pekka Marttinen

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Original language	English
Title of host publication	Conference on Uncertainty in Artificial Intelligence (UAI 2020)
Publisher	JMLR
Volume	124
Publication status	Published - 2020
MoE publication type	A4 Conference publication
Event	Conference on Uncertainty in Artificial Intelligence - Virtual, Online Duration: 3 Aug 2020 → 6 Aug 2020 Conference number: 36

Publication series

Name	Proceedings of Machine Learning Research
Publisher	PMLR
ISSN (Electronic)	2640-3498

Conference

Conference	Conference on Uncertainty in Artificial Intelligence
Abbreviated title	UAI
City	Virtual, Online
Period	03/08/2020 → 06/08/2020

Access to Document

Cite this

@inproceedings{68a1a46ad96f4dcc918f15e46ecdda80,

title = "Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation",

author = "Marko J{\"a}rvenp{\"a}{\"a} and Aki Vehtari and Pekka Marttinen",

year = "2020",

language = "English",

volume = "124",

series = "Proceedings of Machine Learning Research",

publisher = "JMLR",

booktitle = "Conference on Uncertainty in Artificial Intelligence (UAI 2020)",

address = "United States",

note = "Conference on Uncertainty in Artificial Intelligence, UAI ; Conference date: 03-08-2020 Through 06-08-2020",

}

Järvenpää, M, Vehtari, A & Marttinen, P 2020, Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. in Conference on Uncertainty in Artificial Intelligence (UAI 2020). vol. 124, Proceedings of Machine Learning Research, JMLR, Conference on Uncertainty in Artificial Intelligence, Virtual, Online, 03/08/2020. <https://arxiv.org/abs/1910.06121>

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. / Järvenpää, Marko; Vehtari, Aki ; Marttinen, Pekka.
Conference on Uncertainty in Artificial Intelligence (UAI 2020). Vol. 124 JMLR, 2020. (Proceedings of Machine Learning Research).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

AU - Järvenpää, Marko

AU - Vehtari, Aki

AU - Marttinen, Pekka

N1 - Conference code: 36

PY - 2020

Y1 - 2020

UR - https://arxiv.org/abs/1910.06121

M3 - Conference article in proceedings

VL - 124

T3 - Proceedings of Machine Learning Research

BT - Conference on Uncertainty in Artificial Intelligence (UAI 2020)

PB - JMLR

T2 - Conference on Uncertainty in Artificial Intelligence

Y2 - 3 August 2020 through 6 August 2020

ER -

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Publication series

Conference

Access to Document

Other files and links

Computational models and methods for deciphering evolutionary patterns in bacterial genomic data

Computational models and methods for deciphering evolutionary patterns in bacterial genomic data

Science-IT

Cite this

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Publication series

Conference

Access to Document

Other files and links

Projects

Computational models and methods for deciphering evolutionary patterns in bacterial genomic data

Computational models and methods for deciphering evolutionary patterns in bacterial genomic data

Equipment

Science-IT

Cite this