Probability bounds analysis for Python

Nicholas Gray; Scott Ferson; Marco de Angelis; Ander Gray; Fracis Baumont de Oliveira

doi:10.1016/j.simpa.2022.100246

Probability bounds analysis for Python

Nicholas Gray, Scott Ferson, Marco de Angelis, Ander Gray, Fracis Baumont de Oliveira

Civil And Environmental Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

78 Downloads (Pure)

Abstract

Probability bounds analysis (PBA) is a collection of mathematical methods generalising interval analysis and probability theory. PBA can be utilised for uncertainty quantification for both aleatory and epistemic uncertainty across a wide range of scientific fields. PBA is most useful when information about variables is only partially known and can be used without requiring untenable assumptions to be made about parameter values, distribution shapes or dependence between variables. This paper introduces a PBA library for the Python programming language.

Original language	English
Article number	100246
Number of pages	6
Journal	Software Impacts
Volume	12
Early online date	12 Feb 2022
DOIs	https://doi.org/10.1016/j.simpa.2022.100246
Publication status	Published - 31 May 2022

Keywords

probability bounds analysis
probability boxes
intervals
uncertainty quantification

Access to Document

10.1016/j.simpa.2022.100246Licence: CC BY 4.0

Gray-etal-SI-2022-Probability-bounds-analysisFinal published version, 0.99 MBLicence: CC BY 4.0

Cite this

@article{dd58a712263d498da836826b805ce144,

title = "Probability bounds analysis for Python",

abstract = "Probability bounds analysis (PBA) is a collection of mathematical methods generalising interval analysis and probability theory. PBA can be utilised for uncertainty quantification for both aleatory and epistemic uncertainty across a wide range of scientific fields. PBA is most useful when information about variables is only partially known and can be used without requiring untenable assumptions to be made about parameter values, distribution shapes or dependence between variables. This paper introduces a PBA library for the Python programming language.",

keywords = "probability bounds analysis, probability boxes, intervals, uncertainty quantification",

author = "Nicholas Gray and Scott Ferson and {de Angelis}, Marco and Ander Gray and {de Oliveira}, {Fracis Baumont}",

year = "2022",

month = may,

day = "31",

doi = "10.1016/j.simpa.2022.100246",

language = "English",

volume = "12",

}

TY - JOUR

T1 - Probability bounds analysis for Python

AU - Gray, Nicholas

AU - Ferson, Scott

AU - de Angelis, Marco

AU - Gray, Ander

AU - de Oliveira, Fracis Baumont

PY - 2022/5/31

Y1 - 2022/5/31

N2 - Probability bounds analysis (PBA) is a collection of mathematical methods generalising interval analysis and probability theory. PBA can be utilised for uncertainty quantification for both aleatory and epistemic uncertainty across a wide range of scientific fields. PBA is most useful when information about variables is only partially known and can be used without requiring untenable assumptions to be made about parameter values, distribution shapes or dependence between variables. This paper introduces a PBA library for the Python programming language.

AB - Probability bounds analysis (PBA) is a collection of mathematical methods generalising interval analysis and probability theory. PBA can be utilised for uncertainty quantification for both aleatory and epistemic uncertainty across a wide range of scientific fields. PBA is most useful when information about variables is only partially known and can be used without requiring untenable assumptions to be made about parameter values, distribution shapes or dependence between variables. This paper introduces a PBA library for the Python programming language.

KW - probability bounds analysis

KW - probability boxes

KW - intervals

KW - uncertainty quantification

U2 - 10.1016/j.simpa.2022.100246

DO - 10.1016/j.simpa.2022.100246

M3 - Article

SN - 2665-9638

VL - 12

JO - Software Impacts

JF - Software Impacts

M1 - 100246

ER -

Probability bounds analysis for Python

Abstract

Keywords

Access to Document

Fingerprint

Cite this