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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2024]
|
| Ausgabe: | 2024 |
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xi, 432 Seiten Diagramme 235 mm |
| ISBN: | 9783031666186 |
Internformat
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| 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 | ||
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Datensatz im Suchindex
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| author | Picci, Giorgio 1942- |
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| bvnumber | BV049921871 |
| classification_rvk | ST 530 |
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| ctrlnum | (OCoLC)1477596515 (DE-599)BVBBV049921871 |
| discipline | Informatik Mathematik |
| edition | 2024 |
| format | Book |
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| id | DE-604.BV049921871 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-28T09:06:45Z |
| institution | BVB |
| isbn | 9783031666186 |
| language | English |
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| oclc_num | 1477596515 |
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| owner_facet | DE-29T |
| physical | xi, 432 Seiten Diagramme 235 mm |
| publishDate | 2024 |
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| publisher | Springer |
| record_format | marc |
| spelling | Picci, Giorgio 1942- Verfasser (DE-588)1089086083 aut An Introduction to statistical data science theory and models Giorgio Picci Cham Springer [2024] xi, 432 Seiten Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier 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 bicssc bisacsh Statistics Machine learning Engineering mathematics Artificial intelligence—Data processing Data Science (DE-588)1140936166 gnd rswk-swf Bayes-Modell (DE-588)1278609571 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Data Science (DE-588)1140936166 s DE-604 Bayes-Modell (DE-588)1278609571 s Erscheint auch als Online-Ausgabe 978-3-031-66619-3 |
| spellingShingle | Picci, Giorgio 1942- An Introduction to statistical data science theory and models bicssc bisacsh Statistics Machine learning Engineering mathematics Artificial intelligence—Data processing Data Science (DE-588)1140936166 gnd Bayes-Modell (DE-588)1278609571 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)1278609571 (DE-588)4056995-0 |
| title | An Introduction to statistical data science theory and models |
| title_auth | An Introduction to statistical data science theory and models |
| title_exact_search | An Introduction to statistical data science theory and models |
| title_full | An Introduction to statistical data science theory and models Giorgio Picci |
| title_fullStr | An Introduction to statistical data science theory and models Giorgio Picci |
| title_full_unstemmed | An Introduction to statistical data science theory and models Giorgio Picci |
| title_short | An Introduction to statistical data science |
| title_sort | an introduction to statistical data science theory and models |
| title_sub | theory and models |
| topic | bicssc bisacsh Statistics Machine learning Engineering mathematics Artificial intelligence—Data processing Data Science (DE-588)1140936166 gnd Bayes-Modell (DE-588)1278609571 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | bicssc bisacsh Statistics Machine learning Engineering mathematics Artificial intelligence—Data processing Data Science Bayes-Modell Statistik |
| work_keys_str_mv | AT piccigiorgio anintroductiontostatisticaldatasciencetheoryandmodels |