Predictive management of macrological infrastructure in an unstable economic environment based on machine learning methods

The article presents developments in predictive management of logistics infrastructure in macrological systems in conditions of high turbulence of the external economic environment, determined by the tightening of the sanctions regime, global slowdown and potential reorientation of foreign economic relations. Global and poorly predictable changes in the parameters of foreign trade cargo flows require the use of adaptive management of logistics infrastructure at the macro level, the development of which is carried out using mathematical support for the implementation of machine learning methods based on the provisions of the Bayesian approach. In particular, the results presented in the article of a detailed analysis of a priori distributions according to the parameters of the models with their subsequent Bayesian comparison, as well as conjugate a posteriori distributions of the parameters of the processes under study, allow us to develop solutions for determining priority areas of accelerated development of the macrological infrastructure, taking into account fluctuations in the main factors.