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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A Review of Classical Statistical Inference,eas and the appropriate technical language which will be used for discussing most problems of data science in this book. We discuss statistical estimation and hypothesis testing in a rather elementary although quite general setting. The specializations to linear models will be discussed in the follo

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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 wi

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excursion

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Some Nonlinear Inference Problems,n, which is for example relevant to scene and motion reconstruction in computer vision. A geometric formulation points to a generalization to spherical manifolds of the familiar Gaussian inference on linear-spaces. Next we discuss in some detail non-linear support vector machines which is a very imp

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objection