0431896 20240111140851.320141023235959.9 84921527707 000358253800035 10.1007/978-3-319-11433-0_35 16 s. E cav_un_epca*0431895978-3-319-11432-38745535-550Cham Heidelberg NewYork Dordrecht LondonSpringer International Publishing2014van der GaagLinda C. FeeldersAd J. Bayesian Networks Probabilistic Inference CP Tensor Decomposition Symmetric Tensor Rank cav_un_auth*0101228 50 Vomlel Jiří UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101212 50 Tichavský Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. PDF soubor http://library.utia.cas.cz/separaty/2014/MTR/vomlel-0431896.pdf 431 kB cav_un_auth*0292670 GA13-20012S GA ČR cav_un_auth*0303443 GA14-13713S GA ČR CZ We propose an approximate probabilistic inference method based on the CP-tensor decomposition and apply it to the well known computer game of Minesweeper. In the method we view conditional probability tables of the exactly l-out-of-k functions as tensors and approximate them by a sum of rank-one tensors. The number of the summands is min{l+1,k-l+1}, which is lower than their exact symmetric tensor rank, which is k. Accuracy of the approximation can be tuned by single scalar parameter. The computer game serves as a prototype for applications of inference mechanisms in Bayesian networks, which are not always tractable due to the dimensionality of the problem, but the tensor decomposition may significantly help. cav_un_auth*0306406 7th European Workshop, PGM 2014, 17.09.2014-19.09.2014 Utrecht NL IN 10000 10200 10201 2015 2 PR RVO:67985556 http://hdl.handle.net/11104/0237777 S RIV/67985556:_____/14:00431896!RIV15-AV0-67985556 152460049 Doplnění UT WOS a Scopus RIV/67985556:_____/14:00431896!RIV15-GA0-67985556 152501092 Doplnění UT WOS a Scopus 2014 84921527707 SCOPUS 000358253800035 WOS PDF soubor 431 kB cav_un_epca*0431895 Probabilistic Graphical Models 978-3-319-11432-3 535 550 Cham Heidelberg NewYork Dordrecht London Springer International Publishing 2014 Lecture Notes in Computer Science 8745 van der Gaag Linda C. 340 Feelders Ad J. 340