Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F13%3A00394473" target="_blank" >RIV/67985556:_____/13:00394473 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/48361143:_____/13:#0000018

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data

Popis výsledku v původním jazyce

Statistical analysis and modelling of various phenomena are well established in nowadays industrial practice. However, the traditional approaches neglecting the true properties of the phenomena still dominate. Among others, this includes also the cases when a variable with bounded range is analyzed using probabilistic distributions with unbounded domain. Since many of those variables nearly ful?ll the basic conditions imposed by the chosen distribution, the properties of used statistical models are violated rather rarely. Still, there are numerous cases, when inference with distributions with unbounded domain may lead to absurd conclusions. The paper addresses this issue from the Bayesian viewpoint. It brie?y discusses suitable distributions and inferential methods overcoming the emerging computational issues.

Název v anglickém jazyce

Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data

Popis výsledku anglicky

Statistical analysis and modelling of various phenomena are well established in nowadays industrial practice. However, the traditional approaches neglecting the true properties of the phenomena still dominate. Among others, this includes also the cases when a variable with bounded range is analyzed using probabilistic distributions with unbounded domain. Since many of those variables nearly ful?ll the basic conditions imposed by the chosen distribution, the properties of used statistical models are violated rather rarely. Still, there are numerous cases, when inference with distributions with unbounded domain may lead to absurd conclusions. The paper addresses this issue from the Bayesian viewpoint. It brie?y discusses suitable distributions and inferential methods overcoming the emerging computational issues.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/7D12004" target="_blank" >7D12004: Pravděpodobnostní monitor distribuovaného průmyslového systému</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 10th international conference on informatics in control, automation and robotics (ICINCO 2013)

ISBN

978-989-8565-70-9

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

380-387

Název nakladatele

INSTICC ? Institute for Systems and Technologies of Information, Control and Communication

Místo vydání

Portugalsko

Místo konání akce

Reykjavík

Datum konání akce

29. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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