Using Consistent Measures of Dependence and Symmetric Rényi Divergence in the Statistical Linearization

The article presents a new approach to identifying input/output mappings of stochastic systems. An essential component of this approach is the use of consistent measures of dependence of random variables. An essential component of this approach is consistent measures of dependence on random variables. The primary tool used in this approach is the symmetrical Rényi divergence. This measure allows us to consider both the properties of the Kullback-Leibler divergence and the Rényi entropy, making it especially useful in identifying the system and constructing sample estimates. One of the key advantages of this approach is its ability to work with dependent data. This is especially important when working with real systems, where inputs and outputs often have some degree of interconnection. The approach proposed in the article to identifying stochastic systems using consistent measures of dependence and symmetric Rényi divergence is significant and has great potential for application in various fields where the identification and analysis of dependencies between input and output variables is required.