0497540 20240111141011.420181204235959.9 8 s. P cav_un_epca*0497537978-80-7464-932-580-87JapanAoyama Gakuin University, Japan2018SungShao-ChinVlachMilan data mining mutual information compositional models conditional independence probability theory cav_un_auth*0216188 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 34 Kratochvíl Václav UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101118 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 33 Jiroušek Radim UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0368377 33 Lee T. R. TW PDF http://library.utia.cas.cz/separaty/2018/MTR/kratochvil-0497540.pdf cav_un_auth*0356801 MOST-18-04 AV ČR CZ TW GA16-12010S GA ČR CZ cav_un_auth*0332303 A compositional model encodes probabilistic relationships among variables of interest. In connection with various statistical techniques, it represents a practical tool for data modeling and data mining. Structure of the model represents (un)conditional independencies among all variables. Relationships of dependent variables are described by low-dimensional probability distributions. Having a compositional model, a data miner can easily apply an intervention on variables of interest, fix values of other variables (conditioning), or to narrow the context of a problem (marginalization). The model learning process can be controlled to avoid overfitting of data. In this paper, we present a new semi-supervised web application that will enable researchers to design probabilistic (compositional) models (both causal and stochastic). Thanks to the web architecture of the system, the researchers will always have a possibility to influence the data-based model construction process from any place of the world. It is also expected that the application of this methodology to practical problems will open new problems that will be an inspiration for further theoretical research. cav_un_auth*0368378 The 21st Czech-Japan Seminar on Data Analysis and Decision Making 20181123 Kamakura JP 20181126 IN 20000 20200 20205 2019 3 PR RVO:67985556 http://hdl.handle.net/11104/0291220 cav_un_auth*0368382 National Chung Hsing University TW S 2018 SCOPUS PUBMED WOS PDF cav_un_epca*0497537 Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making Aoyama Gakuin University, Japan 2018 Japan 80 87 978-80-7464-932-5 340 Sung Shao-Chin 340 Vlach Milan