Methodological Basis of Causal Forecasting of the Economic Systems Development Management Processes Under the Uncertainty

Autor:	Olga Gonchar, Juliya Burenko, Marharyta Sharko, Nestor Shpak, Kateryna Vorobyova, Olena Lepokhina
Rok vydání:	2020
Předmět:	Bayes' theorem Variables Economic indicator Computer science media_common.quotation_subject Information processing A priori and a posteriori Entropy (information theory) Bayesian network Economic system Associative property media_common
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030542146 ISDMCI
DOI:	10.1007/978-3-030-54215-3_27
Popis:	The methodological bases of information support of the contribution of economic development indicators for variables input are proposed. Structural diagrams, directions and quantitative estimates of the causal relationships of the factors affecting the output dependent variable are developed. It is suggested that the difference between apriori and aposteriori entropy of economic system indicators is to be taken as a measure of removing information uncertainty. To improve the accuracy of quantitative estimates of the likelihood of linkages between economic indicators and input variables, we propose to use the Bayes formula. The algorithm of causal prediction is presented. It is recommended to model the forecasting situations using Bayesian networks, which allow us to encode knowledge of causal and associative relationships to make management decisions for the development of complex economic systems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::40829bf4c1b21c255fa478d8f9500bef https://doi.org/10.1007/978-3-030-54215-3_27 Zobrazit plný text záznamu