Titlebook: An Introduction to Statistical Data Science; Theory and Models Giorgio Picci Textbook 2024 The Editor(s) (if applicable) and The Author(s),

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Tamotsu Mukaiyachi,Toshikazu Katois testing and some traditional applications such as analysis of variance, while the second part points to the “modern” view of linear decision theory centering on linear separability and related algorithms such as the theory of optimal separating hyperplanes. A clever generalization of this idea will lead to nonlinear support vector machines.

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Giorgio PicciPresents statistical concepts, models, methods and techniques for data science.Provides mathematical derivations of algorithms and procedures.Benefits graduate students in applied mathematics and engi

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Linear Hypotheses and Linear Discriminant Analysis,is testing and some traditional applications such as analysis of variance, while the second part points to the “modern” view of linear decision theory centering on linear separability and related algorithms such as the theory of optimal separating hyperplanes. A clever generalization of this idea will lead to nonlinear support vector machines.

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Bayesian Statistics, is an a priori information of probabilistic nature about the variable . which is the object of the statistical inference problem. making it a . which, by its very nature, cannot be assigned an exact numerical value. Many problems in econometrics and engineering have a natural formulation in the Bayesian context.

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