An Introduction to Statistical Data Science Theory and Models /
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...
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| Tác giả của công ty: | |
| Định dạng: | Điện tử eBook |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Phiên bản: | 1st ed. 2024. |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://doi.org/10.1007/978-3-031-66619-3 |
| Các nhãn: |
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| 245 | 1 | 3 | |a An Introduction to Statistical Data Science |h [electronic resource] : |b Theory and Models / |c by Giorgio Picci. |
| 250 | |a 1st ed. 2024. | ||
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| 300 | |a XI, 432 p. 42 illus., 28 illus. in color. |b online resource. | ||
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| 505 | 0 | |a - 1. Introduction -- 2. Classical Statistical Inference -- 3. Linear Models -- 4. Conditioning and Regularization -- 5. Linear Hypotheses and LDA -- 6. Bayesian Statistics -- 7. Principal Component Analysis -- 8. Non Linear Inference -- 9. Time Series. | |
| 520 | |a 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, including material on neural networks, the book concludes with a discussion on time series analysis and estimating their dynamic models. Featuring examples and exercises partially motivated by engineering applications, this book is intended for graduate students in applied mathematics and engineering with a general background in probability and linear algebra. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Bayesian Inference. |
| 650 | 2 | 4 | |a Statistical Learning. |
| 650 | 2 | 4 | |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 650 | 2 | 4 | |a Engineering Mathematics. |
| 650 | 2 | 4 | |a Data Science. |
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