| 期刊全稱 | An Introduction to Statistical Data Science | | 期刊簡稱 | Theory and Models | | 影響因子2023 | Giorgio Picci | | 視頻video | http://file.papertrans.cn/168/167426/167426.mp4 | | 發(fā)行地址 | Presents statistical concepts, models, methods and techniques for data science.Provides mathematical derivations of algorithms and procedures.Benefits graduate students in applied mathematics and engi | | 圖書封面 |  | | 影響因子 | .This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data. Aimed at young scientists, and motivated by their scientific prospects, it provides first principle derivations of various algorithms and procedures, thereby supplying a solid background for their future specialization to diverse fields and applications...The beginning of the book presents the basics of statistical science, with an exposition on linear models. This is followed by an analysis of some numerical aspects and various regularization techniques, including LASSO, which are particularly important for large scale problems. Decision problems are studied both from the classical hypothesis testing perspective and, particularly, from a modern support-vector perspective, in the linear and non-linear context alike. Underlying the book is the Bayesian approach and the Bayesian interpretation of various algorithms and procedures. This is the key to principal components analysis and canonical correlation analysis, which are explained in detail. Following a chapter on nonlinear inference | | Pindex | Textbook 2024 |
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