Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00380991" target="_blank" >RIV/67985556:_____/12:00380991 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition
Original language description
Conditional probability tables (CPTs) of threshold functions represent a generalization of two popular models ? noisy-or and noisy-and. They constitute an alternative to these two models in case they are too rough. When using the standard inference techniques the inference complexity is exponential with respect to the number of parents of a variable. In case the CPTs take a special form (in this paper it is the noisy-threshold model) more efficient inference techniques could be employed. Each CPT defined for variables with finite number of states can be viewed as a tensor (a multilinear array). Tensors can be decomposed as linear combinations of rank-one tensors, where a rank one tensor is an outer product of vectors. Such decomposition is referred toas Canonical Polyadic (CP) or CANDECOMP-PARAFAC (CP) decomposition. The tensor decomposition offers a compact representation of CPTs which can be efficiently utilized in probabilistic inference.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of The Sixth European Workshop on Probabilistic Graphical Models
ISBN
978-84-15536-57-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
355-362
Publisher name
DECSAI, University of Granada
Place of publication
Granada
Event location
Granada
Event date
Sep 19, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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