Three steps in the development of the maximum likelihood (ML) method are presented. At first, the application of the ML method and Fisher information notion in the model selection analysis is described (Chapter 1). The fundamentals of differential geometry in the construction of the statistical space are introduced, illustrated also by examples of the estimation of the exponential models. At second, the notions of the relative entropy and the information channel capacity are introduced (Chapter 2). The observed and expected structural information principle (IP) and the variational IP of the modified extremal physical information (EPI) method of Frieden and Soffer are presented and discussed (Chapter 3). The derivation of the structural IP based on the analyticity of the logarithm of the likelihood function and on the metricity of the statistical space of the system is given. At third, the use of the EPI method is developed (Chapters 4-5). The information channel capacity is used for the field theory models classification. Next, the modified Frieden and Soffer EPI method, which is a nonparametric estimation that enables the statistical selection of the equation of motions of various field theory models (Chapter 4) or the distribution generating equations of statistical physics models (Chapter 5) is discussed. The connection between entanglement of the momentum degrees of freedom and the mass of a particle is analyzed. The connection between the Rao-Cramer inequality, the causality property of the processes in the Minkowski space-time and the nonexistence of tachions is shown. The generalization of the Aoki-Yoshikawa sectoral productivity econophysical model is also presented (Chapter 5). Finally, the Frieden EPI method of the analysis of the EPR-Bhom experiment is presented. It differs from the Frieden approach by the use of the information geometry methods.
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