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020 _a9783031666193
_9978-3-031-66619-3
024 7 _a10.1007/978-3-031-66619-3
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
100 1 _aPicci, Giorgio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985759
245 1 3 _aAn Introduction to Statistical Data Science
_h[electronic resource] :
_bTheory and Models /
_cby Giorgio Picci.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXI, 432 p. 42 illus., 28 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 _aThis 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.
650 0 _aStatistics.
_965982
650 0 _aStatistics .
_965982
650 0 _aMachine learning.
_955059
650 0 _aEngineering mathematics.
_964145
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aStatistical Theory and Methods.
650 2 4 _aBayesian Inference.
_976436
650 2 4 _aStatistical Learning.
_967991
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_966911
650 2 4 _aEngineering Mathematics.
_964145
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031666186
776 0 8 _iPrinted edition:
_z9783031666209
776 0 8 _iPrinted edition:
_z9783031666216
856 4 0 _uhttps://doi.org/10.1007/978-3-031-66619-3
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c64066
_d64066