Introduction to Probability (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
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"… a welcome addition … The authors–wisely, in this reviewer’s opinion–take special care to maintain a conversational tone to prioritize accessibility instead. The result is a very readable text with concepts introduced with a degree of clarity that should suit the beginner extremely well. … An additional feature is the extensive use, and related instruction, of the R programming language for computations, simulations, approximations, and so forth. … beginning students opting for easy-paced learning will find the book highly suited to the purpose … An e-book version of the book is available upon creating an account with the website vitalsource.com and redeeming a code provided with every print copy."
―International Statistical Review, 83, 2015
"A few months ago I reviewed Blitzstein and Hwang’s excellent modern Introduction to Probability, which is chock full of features to ease the student’s path. … Blitzstein and Hwang try everything possible to help the student understand the material. … Blitzstein and Hwang have problems with applications to just about anything you can think of … What it comes down to, in my opinion, is that Blitzstein and Hwang is an excellent book for a wide variety of audiences trying to learn probability."
―Peter Rabinovitch, MAA Reviews, October 2015
"Introduction to Probability is a very nice text for a calculus-based first course in probability. … The exercises are truly impressive. There are about 600 and some of them are very interesting and new to me. … The website has R code, the previously mentioned solutions, and many videos from the authors teaching the class. The videos are entertaining as well as informative. … In addition to the standard material for such a course, there are also very nicely done chapters on inequalities and limit theorems, Markov chains, and Markov chain Monte Carlo. … this is an excellent text and deserves serious consideration."
―MAA Reviews, August 2015
"Unique in its conceptual approach and its incorporation of simulations in R, this book is a welcome addition to the vast collection of probability textbooks currently available. … The topics covered in the book follow a fairly traditional order … The companion website for this textbook, stat110.net, offers supplemental materials to the textbook. There are more than 600 exercises in the textbook, and 250 of these exercises have detailed solutions available on the website. The website offers additional handouts and practice problems and exams, as well as over 30 video lectures available on YouTube or iTunes U. The book is also available as an electronic book. Overall, Introduction to Probability offers a fresh perspective on the traditional probability textbook. Its sections on simulation in R, emphasis on common student mistakes and misconceptions, story-like presentation, and illuminating visualizations provide a comprehensive, well-written textbook that I would consider using in my own probability course."
―The American Statistician, August 2015
"Full of real-life motivations and applications, this is a leisurely paced, exercise-laden text, which is also suitable for self-study. Each chapter ends with a Recap section, another section with R code snippets suggesting how to perform calculations and simulations with that program, and finally an Exercises section with an unusually large amount of exercises. Supplementary material is provided ... The book includes a redemption code providing access to an e-book version of the text ..."
―Zentralblatt MATH 1300
About the Author
Joseph K. Blitzstein, PhD, professor of the practice in statistics, Department of Statistics, Harvard University, Cambridge, Massachusetts, USA
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Background: I have a bachelors in Applied Mathematics. I'm a terrible auditory learner; lectures/tutorials do nothing for me. Self-studying is how I got through my entire degree (I was on the Dean's List). Working through books cover to cover is how I learn, hence I desire my books to be both (a) self-contained, and (b) well paced. The low rating isn't necessarily due to bad content, but how useful the book was for me to self-study.
As a quick list of things I liked:
-The book focuses more on intuition than computation
- Challenging problem sets.
- The section on conditional expectation should be required reading for anyone learning statistics
The negatives, from a self-studying perspective, are grave.
1. Bad problem sets for self-study.
The problem sets in this book are designed to get you thinking, but for self-studying they are poorly designed. This is a combination of a) Almost no lateral movement in difficulty and b) no answers to easier problems. The problem with (a) is that these problem sets are time consuming. You aren't going to have enough hours in the week to even attempt half the problems in a given problem set. The problem with (b) is that the book often puts the onus on the learner to understand the extent of their own ignorance, but catch is that if you are using this book as a legitimate introduction then you won't know the extent of what you don't know. That is, it's very easy to be sure of the wrong answer when you know very little. By far the worst experience I've had in this book was working through several problems only to get to the first worked solution and realize I did every previous problem wrong--this can be utterly demoralizing and sap your will to study.
Take the section on moments. This section is 25 pages long (This is NOT an easy book, if some sections don't click then it isn't uncommon to only progress a few pages in an hour), 25 questions and only four solutions! The first solution is the 13th problem.
2. Poor pacing.
This book covers a lot of theory, but what is worse is that you have to wait till the end of a chapter to see the problem sets which can easily mean 30-40pages of theory before you see your first problem. By then you are very likely to forget some, but crucial, information that makes the problem sets all the more frustrating. The problem sets are done so that they often combine certain parts of the section, so it isn't easy to skip to the problem sets and do the ones you *think* you can do..
Remember, this is not an easy book, it isn't uncommon to spend an hour and only get through several pages as you try understand a difficult concept. Having to work through 30+ pages before you get your first problem, for me, is unreasonable.
3. The author puts too much emphasis on his own intuition.
There will be times where you read a long-winded explanation where you spend just as much time trying to figure out the logic of his example as you do learning the content of what he is trying to explain. He has his own internal logic that makes perfect sense to him, but if you aren't on the same wavelength then it feels like you are trying to decipher hieroglyphics. A good example of this is when he tries to explain the linearity of expectation of two different random variables using pebbles, his unnecessarily verbose and "intuitive" proof made something fairly simple difficult to understand.
If this authors pedagogy speaks to you then great, but at times I found myself spending more time trying to understand the author than the material.
If you want to self-study using this book then make sure you have a lot of time on your hands. If you're at Harvard and have access to teaching assistants that can help then it should be fine, but if you are by yourself then the amount of time you spend on a section can multiply greatly without someone knowledgeable to help you with what you don't understand.
If you would like some other recommendations try:
1. Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik
2. Probability and Statistics, Degroot and Schervish
The first book you can actually view free online--it's a good book but lacks on the exposition. The second is a book on Mathematical Statistics, but it comprehensively covers the introductory probability topics in great detail. It is about 900 pages long and the first 400 are on introductory probability topics (from counting to the central limit theorem).
One of the pluses is that one of the authors of this book has posted his lecture videos online, which are based on this book. This brings the material to life in a way that other textbooks can't match.
Another major plus is that the solutions provided to selected problems are actually explained in-depth, unlike some other textbooks which just present you with the final answer. I've noticed a relatively high percentage of the problems in this book are truly thought-provoking, unlike other textbooks where problems are usually more plug-and-chug. As a result, I've gotten a lot more out of doing problems in this book than others.
Another big plus is that the writers incorporate their obvious wealth of teaching experience into their exposition of various topics. Many times, where other textbooks cease their explanation of a topic, this textbook will continue by giving examples of common misconceptions that students have -- and if you're learning this material for the first time, you'll probably find that at least some of these misconceptions coincide with your own. This is more than a mere convenience -- it chisels your understanding of the material into something much more precise than you would get from other, less verbose textbooks.
In summary, I strongly recommend this book to people trying to learn about this subject matter for the first time. Just make sure you have the required Calculus background.
The practise problems have an excellent difficulty gradient, and I strongly advocate that those serious in studying the topic by themselves to simply do the problems in numerical order. The solution manual available on the internet give the solutions for the most pedagogically useful problems, and is also a must-have.
I give it 5 stars because there are some astoundingly good ideas and exercises in here. The practice on indicator random variables and conditional expectation alone probably justifies the book's price. The (optional) heuristic in terms of viewing the conditional expectation in terms of Gramm Schdmit and geometry, and also the optional item on Probabilistic Existence proofs are extremely good stuff that are not normally discussed in an accessible way. (I had some prior vague familiarity but the ideas 'clicked' after reading this book.) They even have Stein-Chen identity as an exercise. Plus several exercises, gently, introducing statistical ideas.
I also really like the little toxic waste signs sprinkled throughout the chapters where they flag egregious errors that people make when misapplying / misinterpreting things in the chapter.
In short, this book covers a ton of good stuff and does so really, really well. If you ever get lost in the weeds in probability, having this book by your side will be quite helpful.
Top international reviews
Definitely worth the purchase for the clear explanations and thinking strategies.
Ich wünschte, dieses Lehrbuch hätte es bereits gegeben, als ich noch auf der Uni war (ist lange Zeit her!). Der Autor hat das unmögliche geschaffen: ein Lehrbuch(!), das man nicht weglegen möchte. Die Erklärungen sind so vielseitig und schaffen es immer wieder, neben der Theorie ein hohes Maß an Intuition zu komplexen Sachzusammenhängen zu entwickeln - im Bereich Wahrscheinlichkeitsrechnung ganz gewiss kein leichtes Unterfangen.
Ich kann nur jedem Studenten im Bereich angewandte und Wirtschaftsmathematik nahe legen, dieses Buch intensiv zu lesen. Ich würde sogar soweit gehen, dies als Pflichtlektüre für jeden Mathelehrer am Gymnasium zu machen. Denn allein die Beispiele und Erklärungen in Kapitel 1 und 2 würden jeden Matheunterricht am Gymnasium enorm bereichern. ... mir sind noch die schrecklichen Mathebücher meiner Söhne in der Kollegstufe in Erinnerung - was für ein Mittelmaß im Vergleich zu diesem Lehrbuch!
Und abschließend möchte ich dieses Buch jedem angehenden "Data Scientist" wärmstens empfehlen. Denn ein intuitives Verständnis von Themen im Bereich der Wahrscheinlichkeitsrechnung ist ein extrem wertvoller Schatz in diesem Metier.
The reading will be made easier by watching Blitzstein's videos of his stats lectures that are posted on YouTube. I recommend that you watch the videos for each section before attempting the readings. Blitzstein is an amazing lecturer, and the videos will make it all much easier to understand. Kudos to Blitzstein and Hwang for making this material accessible.
However, as others have noted, the book I received was partially damaged. The construction of the spine appears to be quite poor, at least on some printings. But with respect to content, this book is 5/5.
I wish I had it in my recommended reading at UNI when I first started learning statistics. It will give you the confidence that you understand why that normal distribution is so important and why it is used all the time. On your way to the central limit theorem you will also learn a lot of useful stuff.