Introduction to Imprecise Probabilities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10246765" target="_blank" >RIV/61989100:27240/21:10246765 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-60166-9_2" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-60166-9_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-60166-9_2" target="_blank" >10.1007/978-3-030-60166-9_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Introduction to Imprecise Probabilities

Popis výsledku v původním jazyce

Since uncertainty is persistent in engineering analyses, this chapter aimed to introduce methods to describe and reason with under uncertainty in various scenarios. Probability theory is the most widely used methodology for uncertainty quantification for a long time and has proven to be a powerful tool for this task. Nevertheless, the construction of stochastic models relies on very fine information, such as large amount of observations, which is not always available. Without it, the constructed models are only very rough approximations of the real laws and may cause incorrect decisions. In this chapter, we introduce other types of models, based on the theory of imprecise probability, which we are able to construct and reason with under situations with limited available knowledge.

Název v anglickém jazyce

Introduction to Imprecise Probabilities

Popis výsledku anglicky

Since uncertainty is persistent in engineering analyses, this chapter aimed to introduce methods to describe and reason with under uncertainty in various scenarios. Probability theory is the most widely used methodology for uncertainty quantification for a long time and has proven to be a powerful tool for this task. Nevertheless, the construction of stochastic models relies on very fine information, such as large amount of observations, which is not always available. Without it, the constructed models are only very rough approximations of the real laws and may cause incorrect decisions. In this chapter, we introduce other types of models, based on the theory of imprecise probability, which we are able to construct and reason with under situations with limited available knowledge.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10100 - Mathematics

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Optimization Under Uncertainty with Applications to Aerospace Engineering

ISBN

978-3-030-60165-2

Počet stran výsledku

44

Strana od-do

35-79

Počet stran knihy

573

Název nakladatele

Springer, Cham

Místo vydání

unknown

Kód UT WoS kapitoly

—