Introduction to Probability

by Joseph K. Blitzstein, Jessica Hwang

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces.

The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Genres: Mathematics, Science, Textbooks, Nonfiction, Technical, Reference, Technology

596 pages, Hardcover

First published August 1, 2014

Reviews

[Deepak · Rating 5 out of 5]

Sometime ago I got interested in machine learning. Since machine learning involves a good amount of statistics, I started looking for books or resources on probability. After abandoning two widely recommended books, I chanced upon Blitzstein's lectures, I felt that his book would have much more to offer than the lectures. I found it immensely interesting, the authors provide lucid explanations for every concept. There is a lot of emphasis on building intuition about statistical concepts. To drive these concepts home, the book has a good collection of exercises, for me these were the most exciting part. The problems are carefully crafted and will make you think, merely plugging in the formula won't work. I would heartily recommend this book as a first course in probability.

[Dan · Rating 4 out of 5]

This book starts from the basic concepts of probability theory and develops it up to the law of large numbers and the central limit theorem (and more). I really like the approach this book takes, with a more intuitive explanation of the concepts rather than just focusing on the mathematics, which often (for me at least) doesn’t provide any idea of *why* these probabilistic results should be true.

The book uses the concept of a “story” to define distributions, which is basically learning by generic example. I find this way more useful than merely stating the density functions, as it becomes a lot easier to recognise when the distributions occur in practice.

The book only relies on basic real analysis, and a tiny bit of linear algebra. As the authors also emphasise: probability isn’t hard mathematics, but what makes it hard is connecting it with the real world and knowing how to apply the vast array of tools available.

[Satyaki Upadhyay · Rating 5 out of 5]

Harvard Stats 110 youtube lectures
Website
edX course

[Hung Tran · Rating 5 out of 5]

5 stars for the effort of giving the intuitive stories for each probability distribution concept.

It does not just introduce the concept but provides many examples of applying probability in real-life situations. Each chapter also has the R codes that connect with the introduced concepts for readers to play around.

Overall, it is one of the best probability books for self-studying, and reading this book while watching the course (https://projects.iq.harvard.edu/stat1...) is highly recommended.

[Anthony Mowers · Rating 5 out of 5]

I discovered that if I wanted to develop a solid foundation in machine learning then a familiarity with the basics of probability theory and statistical inference would be useful.

This book teaches basic probability and several of the more common statistical distributions in an academic but easy to read style. It's a good prep before learning statistical inference.

Prerequisite is only first year university level mathematics skills.

[Hanyu · Rating 5 out of 5]

Absolutely amazing. Crisp and clean introduction. Questions are somewhat challenging.

[Oleg Dats · Rating 5 out of 5]

Perfect supplementary material for the amazing course stats-101 by Blitzstein.
https://projects.iq.harvard.edu/stat1...

[Renan De Oliveira Pereira · Rating 5 out of 5]

Great book. Pair it with STAT110 and you have a huge amount of material for self-study. Book and video lectures are both suited for refreshment and for a first contact with the subject.

[Alyssa · Rating 5 out of 5]

Thank you, Professor Blitzstein!

[dorecorn · Rating 4 out of 5]

very verbose. but explains concepts well

[Sebestyén · Rating 5 out of 5]

The best intuition builder in probability theory by far. Covers much more than a typical introductory-level text and has such a clearness in explanation than nothing else.

[Wendelle · Rating 5 out of 5]

through this beautiful, explanatory book, i finally understood whatever it was that was going on in the formalism behind john a. rice's textbook.

[Hnank El · Rating 5 out of 5]

Great book, very interesting, quite difficult though

[Goo · Rating 4 out of 5]

I read most of it for an introductory course on probability theory. Ever since then I reread parts of it whenever I need to refresh basic intuitions.

I couldn't imagine a better introduction to probability theory.

[Kent Sibilev · Rating 5 out of 5]

Amazing course and one of the best introductions to the theory of probability.

[Wojtekwalczak · Rating 5 out of 5]

Loved both the book and the lectures.

[Xi Gong · Rating 5 out of 5]

Without much exaggeration, this is the single most important book I have read in my life. It introduced me to the possibility of extending logic to the realm of unpredictability and chaos. The abundant amount of concrete and vivid examples helped me to appreciate many of the core concepts of probability and statistics. I sincerely recommend this book to anyone who wants a lucid, fascinating, and at times challenging read.