Probability, Random Variables, and Data Analytics with Engineering Applications

This book provides a balanced mix of traditional topics and data analytics, expanding the scope, diversity, and applications of engineering probability. It includes Excel spreadsheets of data and a solutions manual for instructors.

Format: Paperback / softback
Length: 473 pages
Publication date: 09 February 2022
Publisher: Springer Nature Switzerland AG

This book is a comprehensive resource for undergraduate engineering students looking to bridge the gap between theory and applications in the field of probability. It offers a unique blend of examples and exercises that utilize data sets, providing students with a practical understanding of probability concepts.

The book begins by introducing fundamental topics such as one and two random variables, transformations, and probability distributions. It then delves into more advanced topics, such as random processes, Markov chains, and stochastic differential equations. Each chapter is accompanied by detailed explanations, examples, and exercises, allowing students to apply the theoretical concepts to real-world scenarios.

One of the key strengths of this book is its emphasis on applications. The authors provide numerous examples and exercises that are relevant to fields such as machine vision, machine learning, and medical diagnostics. These examples help students connect the theoretical concepts with practical applications, making the contents of the book more engaging and relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies.

In addition to the theoretical content, the book also includes a full suite of classroom material, including lecture slides, problem sets, and solutions manual. This material is designed to assist instructors in teaching the course. The solutions manual provides detailed explanations of the solutions to all the exercises, making it an invaluable resource for both students and instructors.

Overall, this book is a must-have for undergraduate engineering students who are interested in developing a strong foundation in probability theory and its applications. Its comprehensive coverage, balanced mix of traditional topics and data analytics, and emphasis on applications make it an invaluable resource for anyone looking to excel in this field.

Weight: 741g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030562618
Edition number: 1st ed. 2021