Introduction to Probability, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition by Joseph K. Blitzstein (PDF)

Description

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and toolsfor 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 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.The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include:Solutions to selected exercisesAdditional practice problemsHandouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110.Links to lecture videos available on ITunes U and YouTube There is also a complete instructor’s solutions manual available to instructors who require the book for a course.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐What a fantastic collection! Thanks to the authors for the engaging, thorough presentation of these problems.

⭐My favorite probability textbook. Look for Joseph Blitzstein’s Stat 110 lectures on YouTube.

⭐Pure Books was absolutely perfect, that simple.

⭐A thorough examination of the topics in a mathematically rigorous manner. Yet the authors do provide plenty of problems with some solutions. Recommended for those interested in the deeper understanding of probability.

⭐The content is great, but the paper and printing quality are below average.

⭐Going through all aspects of probability thoroughly.Much more content compared to the online stats110 video.Definitely worth it if you’ve watched the stats110 video or you are interested in probability or data science!

⭐I am a working data scientist but wanted to brush up on “classical” probability type problems, which I don’t get to deal with at work basically ever. This book provides such clear explanations and so many problems (many with solutions that can be found online) it’s a joy to study. Can’t recommend highly enough!

⭐The content of the book is very good. However, the quality of the print is terrible. They had big problems with faded ink and in some places the print is barely legible. I can’t believe they let this defective printing be released.

⭐One of the best probability textbooks in the market.But the new book’s back cover was squeezed.

⭐Educational.

⭐Very good

⭐The Introduction to Probability by Blitzstein and Hwang is the best introductory level probability book in the market. They way Prof. Blitzstein explains basic definitions such as random variable and the probability function (probability set function) is brilliant. It’s a great combination with the Stat110 course on either Youtube or edX. This book goes from basics to depth of the topics and the problem sets are just great. There’s a total of 602 exercises, though not many people really went through each single one, but they’re worth trying. Some of these are extremely difficult but very interesting. Also the structure of this book offers a good connection to introductory level of mathematical statistics. I own several different probability textbooks, including a famous MIT one, but this one stands out as the BEST.

⭐I’m reviewing this book from the perspective of a (business) Data Scientist. The math is often way over my head (and need), but I’m a huge fan of simulations and R.This book covers lots of simulations (Monte Carlo, Bootstrapping, Markov Chains …). I find that bootstrapping and permutations are rarely covered in traditional statistics books. That’s sad. By using R, I believe that it’s relatively easy to learn. See also “Statistical Inference via Data Science.”Personal highlights:Pages 11/31/42/138: the parts of random sampling with replacement (bootstrapping, which is a variation of a Monte Carlo simulation) are lucid, beautiful, and often combined with excellent samples in R code.Page 457: loved the part on the limitations of sampling and alternatives.Page 484: simulating pi with a Monte Carlo simulation in R. How cool is that?Page 559: excellent explanation of the Poisson process with samples in R.If you’re in Data Science and want to push your math skills, this is definitely a great book.FrancoPS: Several parts reminded me of “Introduction to Data Science,” which was also written by a Harvard professor. In my view, both authors are academic rock stars. What a privilege to be able to learn from them.

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