0427059 20240103204112.820140618235959.9 000334087400010 10.1016/j.ijar.2013.12.002 21 s. P cav_un_epca*02567740888-613X554 (2014)1072-1092Elsevier Bayesian networks Probabilistic inference Candecomp-Parafac tensor decomposition Symmetric tensor rank cav_un_auth*0101228 Vomlel Jiří Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/MTR/vomlel-0427059.pdf GA13-20012S GA ČR cav_un_auth*0292670 GA102/09/1278 GA ČR cav_un_auth*0253174 The specification of conditional probability tables (CPTs) is a difficult task in the construction of probabilistic graphical models. Several types of canonical models have been proposed to ease that difficulty. Noisy-threshold models generalize the two most popular canonical models: the noisy-or and the noisy-and. When using the standard inference techniques the inference complexity is exponential with respect to the number of parents of a variable. More efficient inference techniques can be employed for CPTs that take a special form. CPTs can be viewed as tensors. Tensors can be decomposed into 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. 2015 JD 2 RVO:67985556 http://hdl.handle.net/11104/0233078 S COMPUTERSCIENCEARTIFICIALINTELLIGENCE 0.683 1.460 Q1 58.39 81.707 Q2 Q1 2014 000334087400010 WOS cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 55 č. 4 2014 1072 1092 Elsevier