Involvement of expert judgement in dynamic model

Krikštolaitis, Ričardas; Lutynska, Anna Ewa; Urbonienė, Sigita; Šarūnienė, Inga

doi:10.1201/b17399-57

Use this url to cite publication: https://hdl.handle.net/20.500.12259/40569

Involvement of expert judgement in dynamic model

Type of publication

Straipsnis konferencijos medžiagoje Web of Science ir Scopus duomenų bazėje / Article in conference proceedings in Web of Science and Scopus database (P1a)

Author(s)

Author	Affiliation
Krikštolaitis, Ričardas	Matematikos ir statistikos katedra / Department of Mathematics and Statistics	LT
Lutynska, Anna Ewa	Gamtos mokslų fakultetas / Faculty of Natural Sciences	LT
Urbonienė, Sigita	Matematikos ir statistikos katedra / Department of Mathematics and Statistics	LT
Šarūnienė, Inga	Matematikos ir statistikos katedra / Department of Mathematics and Statistics	LT

Title

Involvement of expert judgement in dynamic model

[en]

Is part of

Safety and reliability: methodology and applications : 24th European conference (ESREL 2014), 14-18 September 2014, Wroclaw, Poland : proceedings / editors Tomasz Nowakowski, Marek Młyńczak, Anna Jodejko-Pietruczuk, Sylwia Werbińska-Wojciechowska. London : CRC Press, 2014

Date Issued

Date
2014

Publisher

London : CRC Press, 2014

Publisher (trusted)

CRC Press

Is Referenced by

Conference Proceedings Citation Index - Science (Web of Science)

Scopus

Taylor & Francis

Extent

p. 393-397

URI

URI
http://www.crcnetbase.com/doi/abs/10.1201/b17399-57
https://hdl.handle.net/20.500.12259/40569

DOI

10.1201/b17399-57

Field of Science

Keywords (lt)

Keywords (en)

Abstract (en)

In many areas models of different processes take into account various factors that are time-dependent and dependent on each other. Thus, it is advisable to construct a dynamic model in order to describe these dependences. In the dynamic model we use indicators. An indicator is a special index, which provides numerical values to important factors for the investigated sector. The values of indicators are obtained from statistical data. Also this dynamic model enables to forecast the dynamics of the indicators according to different new factors. Since the parameters of new factors are not exactly known (got from expert judgement), their influences on indicators are expressed as random variables with known probabilistic distributions. Indicators model based on historic data is adjusted by probabilistic model with the influence of new factors on indicators using a Bayesian method.