The laws of big numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion are all covered in this book's standard introduction to probability theory.

It is a thorough analysis that focuses on the outcomes that are most helpful for applications. There are 200 examples and 450 puzzles because the author believes that applying probability to real-world situations is the best way to learn about it.

This book is a valuable resource as well. Several interesting and concrete examples are presented throughout the textbook, which will help novices obtain a better understanding of the fundamentals of probability theory

The book's collection of instances is its strongest point. The author has done a remarkable job of demonstrating both the uses and limitations of the theorems that are provided.

A new chapter on multidimensional Brownian motion and its connections to partial differential equations (PDEs), a subject that is discovering new applications, is included in the new edition of this engaging but rigorous introduction to measure theoretic probability theory, which is intended for use in a graduate course.