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Probability: With Applications and R, by Robert P. Dobrow
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An introduction to probability at the undergraduate level
Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.
With real-life examples and thoughtful exercises from fields as diverse as biology, computer science, cryptology, ecology, public health, and sports, the book is accessible for a variety of readers. The book’s emphasis on simulation through the use of the popular R software language clarifies and illustrates key computational and theoretical results.
Probability: With Applications and R helps readers develop problem-solving skills and delivers an appropriate mix of theory and application. The book includes:
- Chapters covering first principles, conditional probability, independent trials, random variables, discrete distributions, continuous probability, continuous distributions, conditional distribution, and limits
- An early introduction to random variables and Monte Carlo simulation and an emphasis on conditional probability, conditioning, and developing probabilistic intuition
- An R tutorial with example script files
- Many classic and historical problems of probability as well as nontraditional material, such as Benford’s law, power-law distributions, and Bayesian statistics
- A topics section with suitable material for projects and explorations, such as random walk on graphs, Markov chains, and Markov chain Monte Carlo
- Chapter-by-chapter summaries and hundreds of practical exercises
Probability: With Applications and R is an ideal text for a beginning course in probability at the undergraduate level.
- Sales Rank: #1255743 in eBooks
- Published on: 2013-10-16
- Released on: 2013-10-16
- Format: Kindle eBook
From the Back Cover
An introduction to probability at the undergraduate level
Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.
With real-life examples and thoughtful exercises from fields as diverse as biology, computer science, cryptology, ecology, public health, and sports, the book is accessible for a variety of readers. The book's emphasis on simulation through the use of the popular R software language clarifies and illustrates key computational and theoretical results.
Probability: With Applications and R helps readers develop problem-solving skills and delivers an appropriate mix of theory and application. The book includes:
- Chapters covering first principles, conditional probability, independent trials, random variables, discrete distributions, continuous probability, continuous distributions, conditional distribution, and limits
- An early introduction to random variables and Monte Carlo simulation and an emphasis on conditional probability, conditioning, and developing probabilistic intuition
- An R tutorial with example script files
- Many classic and historical problems of probability as well as non-traditional material, such as Benford's law, power-law distributions, and Bayesian statistics
- A topics section with suitable material for projects and explorations, such as random walk on graphs, Markov chains, and Markov chain Monte Carlo
- Chapter-by-chapter summaries and hundreds of practical exercises
Probability: With Applications and R is an ideal text for a beginning course in probability at the undergraduate level.
About the Author
ROBERT P. DOBROW, PhD, is Professor of Mathematics at Carleton College. He has taught probability for over fifteen years and has authored numerous papers in probability theory, Markov chains, and statistics.
Most helpful customer reviews
5 of 5 people found the following review helpful.
One of the good applied R probability books
By I Teach Typing
There are two good probability with R books on the market, this one and Probability with R: An Introduction with Computer Science Applications. Both are nicely written but they are designed for different audiences. Where Probability with R is readable by any educated audience, it has its examples firmly embedded in computer science. This one has appealing examples from all over the map. In probability with R the code is in the foreground. Here the R code is relegated to code insert boxes. Although the code is deemphasized it is present throughout and the website that accompanies the book has the script files. Beyond these two points, the largest difference between the two books is the emphasis on math. Probability with R is nowhere as mathematically rigorous. This book is designed for readers who have completed multivariate calculus and the math is prominent throughout. Each chapter has a set of probability problems. Sadly the author only includes solutions to half and the solution only has the answer not steps (code) needed to get the solution.
So, get this book if you are looking for engaging examples that are covered with code and a solid dose of math. If you are looking for more thorough coverage of R, get Probability with R instead.
4 of 5 people found the following review helpful.
Good for math/stat majors wanting theory fundamentals
By Jesse Walker
As statistics books go, this one's actually readable. As a doctoral candidate in biology (with at best a basic understanding of calculus), I could follow along. That said, it's academically focused on theory, not practice. This is not the book you want if you aren't comfortable with mathematical (and probabilistic) equations and aren't interested in the theory behind probability. If you just want to be able to apply the stuff to practical problems, this book will get you there, but with way more theory than needed.
It's interesting, and a good book, if you want the mathematical and statistical foundations of probability theory. Explanations are good, examples are straightforward (except Example 1.23; there are two thinking errors in that one, leading to an incorrect answer), and R code isn't terribly hard to find to allow simulation to follow the examples in the book (Note: getting this book and following along will not teach you R). I would have benefited a bit more if the text were either dumbed down a bit (more english, less 'math'), or if my math background were more rigorous.
If you're mathematically-inclined, and want a good primer on probability theory, this would be a good choice. If you're looking to learn R, or want something more easily understood by those without a rigorous math background, this ain't your book.
2 of 2 people found the following review helpful.
Confusing and obscure
By avid reader
I know that the book is first-edition, and so there are already revisions to be made, but the format makes the material hard to understand. For example, the book will provide a sample question and begin to explain how to solve it, and then will delve into some other tangent, only to return to a solution to a completely different problem that was never even stated!!! The textbook lacks sufficient examples to guide the student to solve the chapter problems. Overall, the book has noticeable typos and a poor structure. I would not recommend unless you already possess a strong background knowledge in probability or reinforce the book with at least one other textbook.
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