Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F22%3APU147154" target="_blank" >RIV/00216305:26110/22:PU147154 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-7390/10/21/3980" target="_blank" >https://www.mdpi.com/2227-7390/10/21/3980</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math10213980" target="_blank" >10.3390/math10213980</a>

Result language

angličtina

Original language name

Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Original language description

This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. The measure of reliability is failure probability. The failure probability is analysed using the Bernoulli distribution with binary outcomes of success (0) and failure (1). Deeper connections between Shannon entropy and variance are explored. Model uncertainties increase the heterogeneity in the data 0 and 1. The article proposes a new methodology for quantifying model uncertainties based on the equality of variance and entropy. This methodology is briefly called "variance = entropy". It is useful for stochastic computational models without additional information. The "variance = entropy" rule estimates the "safe" failure probability with the added effect of model uncertainties without adding random variables to the computational model. Case studies are presented with seven variants of model uncertainties that can increase the variance to the entropy value. Although model uncertainties are justified in the assessment of reliability, they can distort the results of the global sensitivity analysis of the basic input variables. The solution to this problem is a global sensitivity analysis of failure probability without added model uncertainties. This paper shows that Shannon entropy is a good sensitivity measure that is useful for quantifying model uncertainties.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA20-01734S" target="_blank" >GA20-01734S: Probability oriented global sensitivity measures of structural reliability</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000882301000001

EID of the result in the Scopus database

2-s2.0-85141695110