bibtype	C - Conference Paper (international conference)
ARLID	0380991
utime	20240111140820.1
mtime	20121101235959.9
title (primary) (eng)	Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition
specification	page_count 8 s. media_type E
serial	ARLID cav_un_epca*0380990 ISBN 978-84-15536-57-4 title Proceedings of The Sixth European Workshop on Probabilistic Graphical Models page_num 355-362 publisher place Granada name DECSAI, University of Granada year 2012
keyword	probabilistic graphical models
keyword	probabilistic inference
keyword	CP tensor decomposition
author (primary)	ARLID cav_un_auth*0101228 name1 Vomlel name2 Jiří full_dept (cz) Matematická teorie rozhodování full_dept (eng) Department of Decision Making Theory department (cz) MTR department (eng) MTR institution UTIA-B full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0101212 name1 Tichavský name2 Petr full_dept (cz) Stochastická informatika full_dept Department of Stochastic Informatics department (cz) SI department SI institution UTIA-B full_dept Department of Stochastic Informatics fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	source_type PDF soubor url http://library.utia.cas.cz/separaty/2012/MTR/vomlel-computationally efficient probabilistic inference with noisy threshold models based on a cp tensor decomposition.pdf source_size 570 kB
cas_special	project project_id GA102/09/1278 agency GA ČR ARLID cav_un_auth*0253174 project project_id GA201/08/0539 agency GA ČR ARLID cav_un_auth*0239648 abstract (eng) 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. action ARLID cav_un_auth*0283704 name The Sixth European Workshop on Probabilistic Graphical Models place Granada dates 19.09.2012-21.09.2012 country ES reportyear 2013 RIV JD num_of_auth 2 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0211567 arlyear 2012 mrcbU56 PDF soubor 570 kB mrcbU63 cav_un_epca*0380990 Proceedings of The Sixth European Workshop on Probabilistic Graphical Models 978-84-15536-57-4 355 362 Granada DECSAI, University of Granada 2012