Polyhedral approach to statistical learning graphical models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00377257" target="_blank" >RIV/67985556:_____/12:00377257 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1142/9789814383462_0020" target="_blank" >http://dx.doi.org/10.1142/9789814383462_0020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1142/9789814383462_0020" target="_blank" >10.1142/9789814383462_0020</a>

Result language
angličtina
Original language name
Polyhedral approach to statistical learning graphical models
Original language description
The statistical task to learn graphical models of Bayesian network structure leads to the study of special polyhedra. In the paper, we offer an overview of our polyhedral approach to learning these statistical models. First, we report on the results on this topic from our recent papers. The second part of the paper brings some specific additional results inspired by this approach.
Czech name
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Czech description
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Project
<a href="/en/project/GA201%2F08%2F0539" target="_blank" >GA201/08/0539: Conditional independence structures: graphical and algebraic approaches</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Harmony of Gröbner Bases and the Modern Industrial Society
ISBN
978-981-4383-45-5
ISSN
—
e-ISSN
—
Number of pages
27
Pages from-to
346-372
Publisher name
World Scientific Press
Place of publication
Singapore
Event location
Osaka
Event date
Jun 28, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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