---
product_id: 8652058
title: "Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)"
price: "E£ 29145"
currency: EGP
in_stock: true
reviews_count: 6
url: https://www.desertcart.com.eg/products/8652058-probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics
store_origin: EG
region: Egypt
---

# Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)

**Price:** E£ 29145
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)
- **How much does it cost?** E£ 29145 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.com.eg](https://www.desertcart.com.eg/products/8652058-probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics)

## 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

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

Review: Perfect companion for users of probability theory - I strongly recommend this book as a reference book for those who apply probability theory in their work: computer scientists, applied statisticians, industrial engineers and so on. This book is very comprehensive: in every topic this book discusses, from very basic undergrad facts to modern probability theory results are all covered. What is remarkable, is that Prof. DasGupta has managed to explain all of these in very high level, avoiding messing up the intuitive message with mathematical jargons. In particular, he avoids the use of measure theory as much as possible, and it is amazing that such a comprehensive book on probability can be written with this much use of measure theory! Therefore, for non-probabilists this is an wonderful reference to extract useful results and intuition from probability theory, without investing too much time on struggling with mathematical techniques. For hard-core mathematics people, this is still a good book for both learn and reference, but for rigorous proofs you should follow the reference given in the book.
Review: Material is okay, very hard to read in kindle - Firstly, let me say that I gave the book one star, so it would average 3 stars. The only other review of this book, which is written by a Purdue professor, is by a Purdue graduate student. He gave the book 5 stars, which I think is overly enthusiastic. Why was he overly enthusiastic, you will have to draw your own conclusions. Firstly, unless you are very happy with the kindle medium, this is not the book for you. It lacks a TOC, which is a huge drawback. There are a certain number of links to figures that work, but navigating through the book is hard. I've read a number of books on kindle, but I still prefer paper because of this issue of 'flipping through' to find something. Beware, this book is only for people who are very okay with the kindle medium. I found navigation particularly difficult. I have only read the section on martingales so far. I have no issues with measure theory (I have a math phd and I actually learned analysis from Zygmund !), but I think the idea of keeping the measure theory out of the exposition is an excellent one. For all but the most serious students of analysis, the measure theory aspect of the subject is a formalism and it just adds confusion. Having said that, I found the exposition to be decent but not crystal clear.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #4,985,898 in Books ( See Top 100 in Books ) #332 in Stochastic Modeling #839 in Computer Simulation (Books) #1,604 in Anatomy & Physiology (Books) |
| Customer Reviews | 3.8 out of 5 stars 9 Reviews |

## Images

![Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics) - Image 1](https://m.media-amazon.com/images/I/612fhzUuiXL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Perfect companion for users of probability theory
*by H***N on November 19, 2011*

I strongly recommend this book as a reference book for those who apply probability theory in their work: computer scientists, applied statisticians, industrial engineers and so on. This book is very comprehensive: in every topic this book discusses, from very basic undergrad facts to modern probability theory results are all covered. What is remarkable, is that Prof. DasGupta has managed to explain all of these in very high level, avoiding messing up the intuitive message with mathematical jargons. In particular, he avoids the use of measure theory as much as possible, and it is amazing that such a comprehensive book on probability can be written with this much use of measure theory! Therefore, for non-probabilists this is an wonderful reference to extract useful results and intuition from probability theory, without investing too much time on struggling with mathematical techniques. For hard-core mathematics people, this is still a good book for both learn and reference, but for rigorous proofs you should follow the reference given in the book.

### ⭐ Material is okay, very hard to read in kindle
*by A***G on January 26, 2013*

Firstly, let me say that I gave the book one star, so it would average 3 stars. The only other review of this book, which is written by a Purdue professor, is by a Purdue graduate student. He gave the book 5 stars, which I think is overly enthusiastic. Why was he overly enthusiastic, you will have to draw your own conclusions. Firstly, unless you are very happy with the kindle medium, this is not the book for you. It lacks a TOC, which is a huge drawback. There are a certain number of links to figures that work, but navigating through the book is hard. I've read a number of books on kindle, but I still prefer paper because of this issue of 'flipping through' to find something. Beware, this book is only for people who are very okay with the kindle medium. I found navigation particularly difficult. I have only read the section on martingales so far. I have no issues with measure theory (I have a math phd and I actually learned analysis from Zygmund !), but I think the idea of keeping the measure theory out of the exposition is an excellent one. For all but the most serious students of analysis, the measure theory aspect of the subject is a formalism and it just adds confusion. Having said that, I found the exposition to be decent but not crystal clear.

### ⭐⭐⭐ Another Potentially Misleading Title
*by B***N on May 8, 2015*

Buyer beware. This may be another probability / statistics text with Machine Learning, or Data Mining, or Flavor of the Month added to the title to boost sales. I have perused the pdf version available through my school's library, but I have not had the text for a course or worked through it. It appears to be a fine text on probability and statistics, but what came to my attention was the apparent lack of any real intention to show how statistical techniques are addressed and used in machine learning application. Chapter 20, Useful Tools for Statistic and Machine Learning, could just as easily had Machine Learning dropped from the title and no one would have noticed. A search of the term "machine" through the pdf doesn't come up until chapter 10 and 11 and then barely again until later chapters, and often only as something as follows: "technique xyz has lately become important to the machine learning community." In my opinion, machine learning appears to have been an afterthought. My point is simply review the contents of the text before buy and know what you are getting.

---

## 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.com.eg/products/8652058-probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics](https://www.desertcart.com.eg/products/8652058-probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics)

---

*Product available on Desertcart Egypt*
*Store origin: EG*
*Last updated: 2026-06-02*