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Understanding Probability
Understanding Probability is a unique and stimulating approach to a first course in probability. The first part of the book demystifies probability and uses many wonderful probability applications from everyday life to help the reader develop a feel for probabilities. The second part, covering a wide range of topics, teaches clearly and simply the basics of probability. This fully revised third edition has been packed with even more exercises and examples and it includes new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion, and a new chapter on continuous-time Markov chains with applications. Here you will find all the material taught in an introductory probability course. The first part of the book, with its easy-going style, can be read by anybody with a reasonable background in high school mathematics. The second part of the book requires a basic course in calculus.
- GenresMathematicsScience
574 pages, Paperback
First published June 14, 2012
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Displaying 1 - 3 of 3 reviews
March 26, 2020
The statistics part is very well explained. Each example problems are also good
May 21, 2024
It was a great book to complement my measure theoretic probability theory class. It's very readable and brings a lot of practical examples.
May 20, 2013
Nowadays we are surrounded by the data whether we want it or not. For example, I never paid any attention how many pages/books i read in a week/month/year. Moreover, arguably i cannot see the value of this information. But I am definitely able now to get this data from this website as well as the analysis by genre, my "preferences" and many other things based upon my reading history. And the same picture is valid for any on-line activity we do…
So I was thinking what it means for myself and how it affects my behaviour. That lead me to the question how these data were used by the others. I read Nate Silver's recent book, but was not very much impressed. He seemed too vague in many areas except for sport. But he mentioned how Bayesian statistics is preferable compared to the traditional one while making predictions. That has lead me to this book. I wanted to refresh my knowledge of the probability theory underlying both applications and to understand the difference between them.
After reading the book I can tell that it certainly helped to satisfy my curiosity. It is a well organised textbook with a lot of practical exercises. Also the book is structured in an unique way - the first part is supposed to give you an "intuitive" understanding of the probability theory and related issues, while the second one is supposed to be more mathematically vigourous. But I personally found them the same in terms of difficulty and using similar maths apparatus. I have to mention that I am familiar with the subject from university, but I never used that knowledge in practical way. So my knowledge were very dusted.
The book is very low on visual help. The graphs are rare, and more formalistic and practical approach seems to be dominant. I struggled with that until I found another little book "Statistics without tears" by D Rowtree. With its help my reading was much more fruitful and it flew at the end. I found some answers about Bayes vs frequentists approach. And I do not think there is such a contradiction between those two techniques like I felt from the Nate Silver's "The Signal and the Noise". Overall it is very interesting area and I plan to continue reading about it.
Since university I forgot, how logical is the probability theory and how beautiful it is! This book has helped to refresh this feeling as well as hunger for knowledge. But this science and its applications is far from intuitive in many ways, especially the conditional probability! I think Daniel Kehneman had a great discussion about that in his superb "Thinking Fast and Slow" without using any high level maths. Personally I still struggle to accept the solution of the famous Monty Hall dilemma:-)
Overall I am sure this book would be great for a student starting the Probability Theory course but the good knowledge of maths up to calculus and some algebra would make his/her read much more enjoyable. Any other person like me needs to be ready to refresh all relevant apparatus as well. But assuming this the book definitely delivers.
So I was thinking what it means for myself and how it affects my behaviour. That lead me to the question how these data were used by the others. I read Nate Silver's recent book, but was not very much impressed. He seemed too vague in many areas except for sport. But he mentioned how Bayesian statistics is preferable compared to the traditional one while making predictions. That has lead me to this book. I wanted to refresh my knowledge of the probability theory underlying both applications and to understand the difference between them.
After reading the book I can tell that it certainly helped to satisfy my curiosity. It is a well organised textbook with a lot of practical exercises. Also the book is structured in an unique way - the first part is supposed to give you an "intuitive" understanding of the probability theory and related issues, while the second one is supposed to be more mathematically vigourous. But I personally found them the same in terms of difficulty and using similar maths apparatus. I have to mention that I am familiar with the subject from university, but I never used that knowledge in practical way. So my knowledge were very dusted.
The book is very low on visual help. The graphs are rare, and more formalistic and practical approach seems to be dominant. I struggled with that until I found another little book "Statistics without tears" by D Rowtree. With its help my reading was much more fruitful and it flew at the end. I found some answers about Bayes vs frequentists approach. And I do not think there is such a contradiction between those two techniques like I felt from the Nate Silver's "The Signal and the Noise". Overall it is very interesting area and I plan to continue reading about it.
Since university I forgot, how logical is the probability theory and how beautiful it is! This book has helped to refresh this feeling as well as hunger for knowledge. But this science and its applications is far from intuitive in many ways, especially the conditional probability! I think Daniel Kehneman had a great discussion about that in his superb "Thinking Fast and Slow" without using any high level maths. Personally I still struggle to accept the solution of the famous Monty Hall dilemma:-)
Overall I am sure this book would be great for a student starting the Probability Theory course but the good knowledge of maths up to calculus and some algebra would make his/her read much more enjoyable. Any other person like me needs to be ready to refresh all relevant apparatus as well. But assuming this the book definitely delivers.
Displaying 1 - 3 of 3 reviews