Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, GB, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoHardback. Condición: New. 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Original o primera edición
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Añadir al carritoHardcover. Condición: Brand New. 296 pages. 10.00x7.25x1.00 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 3 working days.
Idioma: Inglés
Publicado por Cambridge University Press CUP, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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EUR 107,96
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Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoCondición: New. 2018. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoCondición: NEW.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoCondición: New. The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second course .
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Idioma: Inglés
Publicado por Cambridge University Press, GB, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: Rarewaves.com UK, London, Reino Unido
EUR 79,78
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Añadir al carritoHardback. Condición: New. 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: preigu, Osnabrück, Alemania
EUR 87,80
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Añadir al carritoBuch. Condición: Neu. High-Dimensional Probability | Roman Vershynin | Buch | Gebunden | Englisch | 2019 | Cambridge University Press | EAN 9781108415194 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: Buchkanzlei, Bremen, Alemania
EUR 58,40
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Añadir al carritoHardcover. Condición: Sehr gut. 300 pp. Very well preserved copy 326 Sprache: Englisch Gewicht in Gramm: 711.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 79,49
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Añadir al carritoHardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: Majestic Books, Hounslow, Reino Unido
EUR 107,33
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Añadir al carritoCondición: New. Print on Demand.
Idioma: Inglés
Publicado por Cambridge University Press, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 110,16
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Añadir al carritoCondición: New. PRINT ON DEMAND.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: CitiRetail, Stevenage, Reino Unido
EUR 80,94
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Añadir al carritoHardcover. Condición: new. Hardcover. 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. The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second course in probability with a view to data science applications, it is also suitable for self-study. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2018
ISBN 10: 1108415199 ISBN 13: 9781108415194
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 115,52
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. 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. The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second course in probability with a view to data science applications, it is also suitable for self-study. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.