Grafoví a algebraičtí reprezentanti modelů podmíněné nezávislosti

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F07%3A00085219" target="_blank" >RIV/67985556:_____/07:00085219 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Graphical and Algebraic Representatives of Conditional Independence Models

Popis výsledku v původním jazyce

The topic of this book chapter is conditional independence models. We review mathematical objects that are used to generate conditional independence models in the area of probabilistic reasoning. More specifically, we mention undirected graphs, acyclic directed graphs, chain graphs, and an alternative algebraic approach that uses certain integer-valued vectors, named imsets. We compare the expressive power of these objects and discuss the problem of their uniqueness. In learning Bayesian networks one meets the problem of non-unique graphical description of the respective statistical model. One way to avoid this problem is to use special chain graphs, named essential graphs. An alternative algebraic approach uses certain imsets, named standard imsets, instead. We present algorithms that make it possible to transform graphical representatives into algebraic ones and conversely. The algorithms were implemented in the R language.

Název v anglickém jazyce

Graphical and Algebraic Representatives of Conditional Independence Models

Popis výsledku anglicky

The topic of this book chapter is conditional independence models. We review mathematical objects that are used to generate conditional independence models in the area of probabilistic reasoning. More specifically, we mention undirected graphs, acyclic directed graphs, chain graphs, and an alternative algebraic approach that uses certain integer-valued vectors, named imsets. We compare the expressive power of these objects and discuss the problem of their uniqueness. In learning Bayesian networks one meets the problem of non-unique graphical description of the respective statistical model. One way to avoid this problem is to use special chain graphs, named essential graphs. An alternative algebraic approach uses certain imsets, named standard imsets, instead. We present algorithms that make it possible to transform graphical representatives into algebraic ones and conversely. The algorithms were implemented in the R language.

Druh

C - Kapitola v odborné knize

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F04%2F0393" target="_blank" >GA201/04/0393: Struktury podmíněné nezávislosti: informačně-teoretický přístup III.</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2007

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Advances in Probabilistic Graphical Models

ISBN

978-3-540-68994-2

Počet stran výsledku

26

Strana od-do

55-80

Počet stran knihy

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Název nakladatele

Springer Verlag

Místo vydání

Berlin Heildeberg

Kód UT WoS kapitoly

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