0531044 20240103224239.420200720235959.9 85089232840 10.1007/978-981-15-4917-5_28 Compositional Models: Iterative Structure Learning from Data 16 s. P cav_un_epca*0531043978-981-15-4916-8Sensor Networks and Signal Processingvol. 176Smart Innovation, Systems and Technologies379-395SingaporeSpringer2021PengSheng-LungFavorskayaMargarita N.ChaoHan-Chieh Compositional models Structure learning Decomposability Likelihood-ratio Test statistics cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ 25 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0393863 Bína Vladislav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101118 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0368377 Lee T. R. TW http://library.utia.cas.cz/separaty/2020/MTR/kratochvil-0531044.pdf GA19-06569S GA ČR CZ cav_un_auth*0380559 MOST-04-18 Akademie věd - GA AV ČR CZ cav_un_auth*0393867 Multidimensional probability distributions that are too large to be stored in computer memory can be represented by a compositional model - a sequence of low-dimensional probability distributions that when composed together try to faithfully estimate the original multidimensional distribution. The decomposition to the compositional model is not satisfactorily resolved. We offer an approach based on search traversal through the decomposable model class using likelihood-test statistics. The paper is a work sketch of the current research. cav_un_auth*0393865 Sensor Networks and Signal Processing (SNSP 2019) /2./ 20191119 Hualien TW 20191122 2022 IN 10000 10200 10201 4 PR RVO:67985556 http://hdl.handle.net/11104/0310094 S 2021 85089232840 SCOPUS PUBMED WOS cav_un_epca*0531043 Sensor Networks and Signal Processing Smart Innovation, Systems and Technologies vol. 176 Springer 2021 Singapore 379 395 978-981-15-4916-8 2190-3018 Peng Sheng-Lung 340 Favorskaya Margarita N. 340 Chao Han-Chieh 340