--- product_id: 104225761 title: "High-Dimensional Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47)" price: "1309 kr" currency: DKK in_stock: true reviews_count: 11 url: https://www.desertcart.dk/products/104225761-high-dimensional-probability-cambridge-series-in-statistical-and-probabilistic-mathematics store_origin: DK region: Denmark --- # High-Dimensional Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) **Price:** 1309 kr **Availability:** ✅ In Stock ## Quick Answers - **What is this?** High-Dimensional Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) - **How much does it cost?** 1309 kr with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.dk](https://www.desertcart.dk/products/104225761-high-dimensional-probability-cambridge-series-in-statistical-and-probabilistic-mathematics) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description desertcart.com: High-Dimensional Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47): 9781108415194: Vershynin, Roman: Books Review: A pedagogical and practical perspective on (modern) probability - Vershynin's book covers a set of topics that is likely to become central in the education for "modern" mathematicians, statisticians, physicists, and (electrical) engineers. He discusses ideas, techniques, and tools that arise across fields, and he conceptually unifies them under the brand name of "high-dimensional probability". His choice of topics (e.g., concentration/deviation inequalities, random vectors/matrices, stochastic processes, etc.) and applications (e.g., sparse recovery, dimension-reduction, covariance estimation, optimization bounds, etc.) delivers a necessary (and timely) addition to the growing body of data-science-related literature—more on this below. Vershynin writes in a conversational, reader-friendly manner. He weaves theorems, lemmas, corollaries, and proofs into his dialogue with the reader without getting caught in an endless theorem-proof loop. In addition, the book's integrated exercises and its prompts to "check!" or think about "why?" are strong components of the book. My copy of the book is already full of notes to myself where I’m “checking” something or explaining “why” something is true/false. (Also, as an aside, I love that coffee cups are used to signal the difficulty of a problem—good style.) I want to highlight a few examples where Vershynin’s choice of topics and his prose shine brightly. In section 4.4.1, he guides us through an example that clearly illustrates the usefulness of ε-nets for bounding matrix norms. I’d seen ε-nets and covering numbers before, but never had good intuition for why they showed up in a proof. Similarly, I’d struggled to gain intuition about why/how Gaussian widths and Vapnik–Chervonekis dimension capture/measure the complexity of a set. After reading sections 7.5 and 8.3 and working through some exercises, the two concepts are much clearer. Moreover, Vershynin connects these ideas back to covering numbers, which helped me better my understanding of all three concepts. Finally, I found the discussions on chaining and generic chaining in chapter 8 to be excellent. Following them up with Talagrand’s comparison inequality, which becomes the hammer of choice for the matrix deviation inequality (in chapter 9), rounds out a long, but very valuable/useful chapter—and one that I’ll certainly re-study and reference. I would recommend this book for those interested in (high-dimensional) statistics, randomized numerical linear algebra, and electrical engineering (particularly, signal processing). As I'm coming to realize, the "concentration of measure" and “deviation inequality” toolbox is essential to these areas. Lastly, I believe that this book makes a great companion to “Concentration Inequalities” by Boucheron, Lugosi, Massart. Review: Very helpful - A great read ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #196,685 in Books ( See Top 100 in Books ) #20 in Statistics (Books) #134 in Probability & Statistics (Books) | | Customer Reviews | 4.8 4.8 out of 5 stars (79) | | Dimensions | 7 x 0.87 x 10 inches | | Edition | 1st | | ISBN-10 | 1108415199 | | ISBN-13 | 978-1108415194 | | Item Weight | 1.57 pounds | | Language | English | | Part of series | Cambridge Series in Statistical and Probabilistic Mathematics | | Print length | 300 pages | | Publication date | September 27, 2018 | | Publisher | Cambridge University Press | ## Images ![High-Dimensional Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) - Image 1](https://m.media-amazon.com/images/I/51iAnQIaNZL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ A pedagogical and practical perspective on (modern) probability *by J***K on December 1, 2018* Vershynin's book covers a set of topics that is likely to become central in the education for "modern" mathematicians, statisticians, physicists, and (electrical) engineers. He discusses ideas, techniques, and tools that arise across fields, and he conceptually unifies them under the brand name of "high-dimensional probability". His choice of topics (e.g., concentration/deviation inequalities, random vectors/matrices, stochastic processes, etc.) and applications (e.g., sparse recovery, dimension-reduction, covariance estimation, optimization bounds, etc.) delivers a necessary (and timely) addition to the growing body of data-science-related literature—more on this below. Vershynin writes in a conversational, reader-friendly manner. He weaves theorems, lemmas, corollaries, and proofs into his dialogue with the reader without getting caught in an endless theorem-proof loop. In addition, the book's integrated exercises and its prompts to "check!" or think about "why?" are strong components of the book. My copy of the book is already full of notes to myself where I’m “checking” something or explaining “why” something is true/false. (Also, as an aside, I love that coffee cups are used to signal the difficulty of a problem—good style.) I want to highlight a few examples where Vershynin’s choice of topics and his prose shine brightly. In section 4.4.1, he guides us through an example that clearly illustrates the usefulness of ε-nets for bounding matrix norms. I’d seen ε-nets and covering numbers before, but never had good intuition for why they showed up in a proof. Similarly, I’d struggled to gain intuition about why/how Gaussian widths and Vapnik–Chervonekis dimension capture/measure the complexity of a set. After reading sections 7.5 and 8.3 and working through some exercises, the two concepts are much clearer. Moreover, Vershynin connects these ideas back to covering numbers, which helped me better my understanding of all three concepts. Finally, I found the discussions on chaining and generic chaining in chapter 8 to be excellent. Following them up with Talagrand’s comparison inequality, which becomes the hammer of choice for the matrix deviation inequality (in chapter 9), rounds out a long, but very valuable/useful chapter—and one that I’ll certainly re-study and reference. I would recommend this book for those interested in (high-dimensional) statistics, randomized numerical linear algebra, and electrical engineering (particularly, signal processing). As I'm coming to realize, the "concentration of measure" and “deviation inequality” toolbox is essential to these areas. Lastly, I believe that this book makes a great companion to “Concentration Inequalities” by Boucheron, Lugosi, Massart. ### ⭐⭐⭐⭐⭐ Very helpful *by S***R on December 11, 2025* A great read ### ⭐⭐⭐⭐⭐ Amazing, clear book *by S***S on May 22, 2021* An *excellent* first treatment of concepts in high-dimensional probability and statistics. The book is very clear and clean, and the many exercises (with helpful hints) make it a good resource for self-study. This has become one of my favorite math textbooks ever! ## Frequently Bought Together - High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) - High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48) - Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 3) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.dk/products/104225761-high-dimensional-probability-cambridge-series-in-statistical-and-probabilistic-mathematics](https://www.desertcart.dk/products/104225761-high-dimensional-probability-cambridge-series-in-statistical-and-probabilistic-mathematics) --- *Product available on Desertcart Denmark* *Store origin: DK* *Last updated: 2026-06-04*