Foundations of compositional models: inference

0542523 - ÚTIA 2022 RIV GB eng J - Článek v odborném periodiku
Jiroušek, Radim - Kratochvíl, Václav - Bína, Vladislav
Foundations of compositional models: inference.
International Journal of General Systems. Roč. 50, č. 4 (2021), s. 409-433. ISSN 0308-1079. E-ISSN 1563-5104
Grant ostatní: GA ČR(CZ) GA19-06569S
Program: GA
Institucionální podpora: RVO:67985556
Klíčová slova: Probability distribution * multidimensionality * marginalization * conditioning * causality * intervention
Obor OECD: Statistics and probability
Impakt faktor: 2.435, rok: 2021
Způsob publikování: Omezený přístup
http://library.utia.cas.cz/separaty/2021/MTR/jirousek-0542523.pdf https://www.tandfonline.com/doi/full/10.1080/03081079.2021.1895142

Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus, the respective apparatus falls fully into probability theory. The present paper surveys the results supporting the design of computational procedures, without which the application of these models to practical problems would be impossible. The methods of inference cannot do without a possibility to focus on a part of the considered multidimensional model and to incorporate data describing the actual situation. Thus, the paper shows how to compute marginals and conditionals of multidimensional models. Also, the paper briefly solves the problem of computing the effect of an intervention, in case the model is interpreted as a causal model.
Trvalý link: http://hdl.handle.net/11104/0320293