Cambridge Series in Statistical and Probabilistic Mathematics: High-Dimensional Probability: An Introduction With Applications in Data Science Series Number 47 download free [PDF and Ebook] by Roman Vershynin
![Cambridge Series in Statistical and Probabilistic Mathematics: High-Dimensional Probability: An Introduction With Applications in Data Science Series Number 47 download free [PDF and Ebook] by Roman Vershynin year 2018](https://compuebooks.com/mega/i/2018/07/cambridge-series-in-statistical-and-probabilistic-mathematics-high-dimensional-probability-an-introduction-with-applications-in-data-science-series-number-47-download-free-pdf-and-ebook-.jpg "Cambridge Series in Statistical and Probabilistic Mathematics: High-Dimensional Probability: An Introduction With Applications in Data Science Series Number 47 download free [PDF and Ebook] by Roman Vershynin year 2018")
- Book name: Cambridge Series in Statistical and Probabilistic Mathematics: High-Dimensional Probability: An Introduction With Applications in Data Science Series Number 47
- Author: Roman Vershynin
- Release date: 2018/7/12
- Publisher: CAMBRIDGE UNIVERSITY PRESS
- Language: English
- Genre or Collection: Computing
- ISBN: 9781108415194
- Rating: 9.89 of 10
- Votes: 523
- Review by: Ariana Tejeda
- Review rating: 8.55 of 10
- Review Date: 2018/9/13
- Total pages: 296
- Includes a PDF summary of 28 pages
- Description or summary of the book: High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.
- Estimated reading time (average reader): 13H38M56S
- Other categories, genre or collection: Econometrics, Signal Processing, Pattern Recognition, Probability & Statistics, Data Analysis: General
- Available formats: LIT, TXTz, DOC, JPG, WORD, EPUB, TXT, PDF. Compressed in Z, AZW, ZIP, CBZ, RAR
- Download servers: ownCloud, Torrent, Dropbox, FreakShare, MEGA, BitShare, Krakenfiles, Mediafire
- Format: Hardback
- Approximate value: 71.45 USD
- Dimensions: 183x260x22mm
- Weight: 710g
- Printed by: Not Available
- Published in: Cambridge, United Kingdom
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