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

0380991 20240111140820.120121101235959.9 Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition 8 s. E cav_un_epca*0380990978-84-15536-57-4Proceedings of The Sixth European Workshop on Probabilistic Graphical Models355-362GranadaDECSAI, University of Granada2012 probabilistic graphical models probabilistic inference CP tensor decomposition 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. PDF soubor http://library.utia.cas.cz/separaty/2012/MTR/vomlel-computationally efficient probabilistic inference with noisy threshold models based on a cp tensor decomposition.pdf 570 kB GA102/09/1278 GA ČR cav_un_auth*0253174 GA201/08/0539 GA ČR cav_un_auth*0239648 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. cav_un_auth*0283704 The Sixth European Workshop on Probabilistic Graphical Models Granada 19.09.2012-21.09.2012 ES 2013 JD 2 RVO:67985556 http://hdl.handle.net/11104/0211567 2012 PDF soubor 570 kB 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