High dimensional probability (87 Ergebnisse)

Hardcover. Zustand: Très bon. Ancien livre de bibliothèque avec équipements. Edition 1998. Tome 43. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1998. Volume 43. Ammareal gives back up to 15% of this item's net price to charity organizations.…

Hardcover. Zustand: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Zustand: New. 2. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

Zustand: New. In English.

Hardcover. Zustand: Gut. 208 pp. Sun-bleached at the spine, name on endpaper, otherwise very well preserved copy 329 Sprache: Englisch Gewicht in Gramm: 568.

Taschenbuch. Zustand: Neu. Neuware - Reactive PublishingModern portfolio construction operates in a high-dimensional regime where the number of assets routinely approaches or exceeds the number of observations. Classical covariance estimators break down in this setting, producing unreliable risk forecasts, distorted principal components, and unstable optimization results.This book develops the mathematical and computational framework needed to address these failures. It presents free probability and random matrix theory as practical tools for spectral analysis, covariance estimation, and risk modeling of large portfolios. Readers move from the Marchenko-Pastur law and free convolution to concrete procedures for cleaning empirical spectra, recovering population eigenvalues, and constructing robust risk measures under realistic market conditions.Core topics include: - Spectral methods for high-dimensional covariance matrices- Free deconvolution and eigenvalue cleaning techniques- Bias-corrected estimators for portfolio risk and factor models- Applications to covariance shrinkage, principal component analysis, and stress testing- Numerical implementation considerations for realistic asset universesThe material is self-contained yet rigorous, bridging theoretical results with the requirements of quantitative portfolio management. It is written for researchers, quant developers, and advanced practitioners who need reliable tools when classical multivariate statistics no longer apply.Vincent Bisette provides a focused treatment of free probability and random matrix methods tailored to the specific challenges of large-scale financial risk modeling.…

Zustand: New. In English.

Hardcover. Zustand: Sehr gut. 300 pp. Very well preserved copy 326 Sprache: Englisch Gewicht in Gramm: 711.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New.

hardcover. Zustand: Sehr gut. 387 Seiten; 9783034804899.2 Gewicht in Gramm: 1.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. 2018. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New. In English.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 296 pages. 10.00x7.25x1.00 inches. In Stock.

Hardcover. Zustand: Brand New. 2nd revised edition edition. 346 pages. 7.00x0.81x10.00 inches. In Stock.

Zustand: New. 2026. 2nd Edition. hardcover. . . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - 'High-Dimensional Probability,' winner of the 2019 PROSE Award in Mathematics, offers an accessible and friendly introduction to key probabilistic methods for mathematical data scientists. Streamlined and updated, this second edition integrates theory, core tools, and modern applications. Concentration inequalities are central, including classical results like Hoeffding's and Chernoff's inequalities, and modern ones like the matrix Bernstein inequality. The book also develops methods based on stochastic processes - Slepian's, Sudakov's, and Dudley's inequalities, generic chaining, and VC-based bounds. Applications include covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, and machine learning. New to this edition are 200 additional exercises, alongside extra hints to assist with self-study. Material on analysis, probability, and linear algebra has been reworked and expanded to help bridge the gap from a typical undergraduate background to a second course in probability.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

Zustand: New.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 348 | Sprache: Englisch | Produktart: Bücher | What is high dimensional probability? Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings.…

Buch. Zustand: 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.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the methods and theory of high dimensional probability, statistics, large-scale optimization, and inference. We aim to quickly bring readers to the frontier and interdisciplinary areas of statistics, optimization, probability, and machine learning. This book covers topics in:High dimensional probability, Concentration inequality, Sub-Gaussian random variables, Chernoff bounds, Hoeffding's inequality, Maximal inequalities, High dimensional linear regression, Ordinary least square, Compressed sensing, Lasso, Variations of Lasso including group lasso, fused lasso, adaptive lasso, etc., General high dimensional M- estimators, Variable selection consistency, High dimensional Optimization, Convex geometry, Lagrange duality, Gradient descent, Proximal gradient descent, LARS, ADMM, Mirror descent, Stochastic optimization, Large-Scale Inference, Linear model hypothesis testing, high dimensional inference, Chi-square test, maximal test, and Higher criticism, False discovery rate control. …