"Learning statistics is one thing; learning to think like a statistician is something else. This book grabs readers by the hand and takes them on a journey through statistics, by the end of which they are well-equipped to think like a statistician. For a book at intermediate level this is no mean feat: ideas are subtle and require a delicate balance of formal mathematics and statistical insight, a balance that is deftly maintained throughout the book. The mathematics is rigorous, but not so much so that underlying intuition is lost. And the statistical concepts themselves are explained with a clarity that is rare in textbooks at a similar level. I wish this book had been available when I was first learning statistics. And I wouldn’t hesitate now to use it as the basis for teaching on any intermediate statistics course. The best compliment I can give is that it was a pleasure to read and gave me new insights, even on material with which I am very familiar. I love this book and congratulate the authors on writing with such clarity and vision. I hope many readers take the opportunity to follow the statistical journey the authors provide."
/~Stuart Coles, author of An Introduction to Statistical Modeling of Extreme Values
“Overall, I give Probability and Statistical Inference: From Basic Principles to Advanced Models a solid thumbs up! It’s well suited as a primary introductory probability theory textbook for undergraduates or applied masters students in statistics or data science. It’s also appropriate as a primary textbook for an advanced survey course in probability and statistics. Further, I recommend this textbook to working professionals in any field who seek further insight on probability theory and statistical inference.”
~Gabriel J. Young, The American Statistician
"This book provides a comprehensive and thorough coverage of probability and distribution theory and statistical inference. Based on a popular undergraduate course at the London School of Economics, the content and its presentation have been honed by the authors over many years of teaching. The result is an extremely clear and engaging text, which achieves that rare balance of explaining statistical concepts in an intuitive and accessible way while maintaining precision and rigour. Concepts are introduced and illustrated using real-world examples, which aids understanding and highlights their practical relevance. The book covers foundational and advanced topics in probability and statistical inference, with an excellent overview of statistical modelling, and detailed treatments of Bayesian approaches and modern simulation-based estimation methods. Each chapter includes an extensive and graduated set of exercises. I highly recommend the book for advanced undergraduate and postgraduate students in statistics and data science, but also as an essential reference for researchers."
/~Fiona Steele, London School of Economics and Political Science
"This book covers a broad range of topics in advanced probability and statistics that are fundamental to a proper understanding of data analysis and statistical modeling. The authors did an excellent job in introducing these topics from basic principles to advanced models in a detailed but approachable style, which makes it possible for senior undergraduates in statistics, mathematics, or related majors to use this book in the first course of probability and statistics so that the students can benefit from the holistic and comprehensive coverage of topics from basic random variables to advanced statistical models."
/~Bing Si, State University of New York at Binghamton
"Learning statistics is one thing; learning to think like a statistician is something else. This book grabs readers by the hand and takes them on a journey through statistics, by the end of which they are well-equipped to think like a statistician. For a book at intermediate level this is no mean feat: ideas are subtle and require a delicate balance of formal mathematics and statistical insight, a balance that is deftly maintained throughout the book. The mathematics is rigorous, but not so much so that underlying intuition is lost. And the statistical concepts themselves are explained with a clarity that is rare in textbooks at a similar level. I wish this book had been available when I was first learning statistics. And I wouldn’t hesitate now to use it as the basis for teaching on any intermediate statistics course. The best compliment I can give is that it was a pleasure to read and gave me new insights, even on material with which I am very familiar. I love this book and congratulate the authors on writing with such clarity and vision. I hope many readers take the opportunity to follow the statistical journey the authors provide."
/~Stuart Coles, author of An Introduction to Statistical Modeling of Extreme Values
“Overall, I give Probability and Statistical Inference: From Basic Principles to Advanced Models a solid thumbs up! It’s well suited as a primary introductory probability theory textbook for undergraduates or applied masters students in statistics or data science. It’s also appropriate as a primary textbook for an advanced survey course in probability and statistics. Further, I recommend this textbook to working professionals in any field who seek further insight on probability theory and statistical inference.”
/~Gabriel J. Young, The American Statistician
