The second edition represents an ongoing effort to make probability accessible to students in a wide range of fields such as mathematics, statistics and data science, engineering, computer science, and business analytics. The book is written for those learning about probability for the first time. Revised and updated, the book is aimed specifically at statistics and data science students who need a solid introduction to the basics of probability.While retaining its focus on basic probability, including Bayesian probability and the interface between probability and computer simulation, this edition's significant revisions are as follows:The approach followed in the book is to develop probabilistic intuition before diving into details. The best way to learn probability is by practising on a lot of problems. Many instructive problems together with problem-solving strategies are given. Answers to all problems and worked-out solutions to selected problems are also provided.Henk Tijms is the author of several textbooks in the area of applied probability. In 2008, he had received the prestigious INFORMS Expository Writing Award for his work. He is active in popularizing probability at Dutch high schools.
Contents:
- Preface
- Combinatorics and Calculus for Probability
- Basics of Probability
- Useful Probability Distributions
- Real-Life Examples of Poisson Probabilities
- Monte Carlo Simulation and Probability
- A Primer on Markov Chains
- Solutions to Selected Problems
- Index
Readership: Undergraduate students in fields such as mathematics, statistics and data science, engineering, computer science and business analytics. Graduate students in natural and social sciences. Students taking a first course in probability, or a course on probability for statistics and data science. High school math teachers and STEM students. Students in natural and social sciences, economics and finance.
Key Features:
- The book uses the approach of probabilistic intuition before getting into details
- An inter-weaved treatment of basic probability and Monte Carlo simulation
- A carefully designed collection of motivational examples and problems
- The student is guided in both creative and algorithmic thinking
