About: Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 to as 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. 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 to as 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. (en)
Title	Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition (en)
skos:prefLabel	Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition (en)
skos:notation	RIV/67985556:_____/12:00380991!RIV13-AV0-67985556
http://linked.open...avai/predkladatel	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GA102/09/1278), P(GA201/08/0539)
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Tichavský, Petr Vomlel, Jiří
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	128301
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/12:00380991
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	probabilistic graphical models; probabilistic inference; CP tensor decomposition (en)
http://linked.open.../riv/klicoveSlovo	CP tensor decomposition probabilistic graphical models probabilistic inference
http://linked.open...ontrolniKodProRIV	[2D4CB54E692F]
http://linked.open...v/mistoKonaniAkce	Granada
http://linked.open...i/riv/mistoVydani	Granada
http://linked.open...i/riv/nazevZdroje	Proceedings of The Sixth European Workshop on Probabilistic Graphical Models
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Advanced methods of blind source separation and blind deconvolution Conditional independence structures: graphical and algebraic approaches
http://linked.open...UplatneniVysledku	2012
http://linked.open...iv/tvurceVysledku	Tichavský, Petr Vomlel, Jiří
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2012-09-19 (xsd:date)
number of pages	8 (xsd:int)
http://purl.org/ne...btex#hasPublisher	DECSAI, University of Granada
https://schema.org/isbn	978-84-15536-57-4
is http://linked.open...avai/riv/vysledek of	Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition

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