Introduction to Probability Theory with Engineering Applications

Aly Farag

ISBN 10: 1793514259 / ISBN 13: 9781793514257

Published by Cognella, Inc Aug 2021, 2021

About this Item

Neuware - Introduction to Probability Theory with Engineering Applications provides students with a solid foundation in probability theory, which deals with the modeling of uncertainty, and illuminates several modern applications of probability in engineering, physics and data analysis.The text is organized into five chapters and three appendices. The opening chapter introduces the notion of probability as a model or representation for the uncertainty associated with statistical experiments. In additional chapters, students learn about random variables through explanations of discrete and continuous variables, conditional distribution, and statistical distribution. Students examine functions of one random variable, two random variables, and extensions to multivariable distributions. The final chapter covers random processes.Helpful appendices include six computer laboratories that correspond with the content in Chapters 2-5, assessment and review questions for each chapter, and basic results from linear algebra.The book is an ideal resource for courses in engineering, computer science, biomedicine, physics, and mathematics. It is also an excellent text for researchers seeking an overview in applied probability theory. It is assumed readers have a background in introductory calculus and computer programming.

Seller Inventory # 9781793514257

Bibliographic Details

• Title: Introduction to Probability Theory with Engineering Applications
• Publisher: Cognella, Inc Aug 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 1793514259
• ISBN 13: 9781793514257
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 203H x 11D millimeters 254 millimeters width by 203 millimeters height by 11 millimeters depth
• Weight: 454 grams

About this title

Synopsis

Introduction to Probability Theory with Engineering Applications provides students with a solid foundation in probability theory, which deals with the modeling of uncertainty, and illuminates several modern applications of probability in engineering, physics and data analysis.

The text is organized into five chapters and three appendices. The opening chapter introduces the notion of probability as a model or representation for the uncertainty associated with statistical experiments. In additional chapters, students learn about random variables through explanations of discrete and continuous variables, conditional distribution, and statistical distribution. Students examine functions of one random variable, two random variables, and extensions to multivariable distributions. The final chapter covers random processes.

Helpful appendices include six computer laboratories that correspond with the content in Chapters 2-5, assessment and review questions for each chapter, and basic results from linear algebra.

The book is an ideal resource for courses in engineering, computer science, biomedicine, physics, and mathematics. It is also an excellent text for researchers seeking an overview in applied probability theory. It is assumed readers have a background in introductory calculus and computer programming.

About the Author

Aly Farag is a professor of electrical and computer engineering at the University of Louisville, where he founded the Computer Vision and Image Processing Laboratory, which focuses on imaging science, computer vision, and biomedical imaging. He holds a Ph.D. in electrical engineering from Purdue University. His main research interests are scene analysis, object reconstruction from multimodality imaging, and statistical and variational methods for object segmentation and registration. He has coauthored over 350 technical papers, a textbook on biomedical imaging, and coedited two volumes on deformable models for biomedical applications. Dr. Farag received the Trustees Award, the top award at the University of Louisville, for his impact on students. He is a Fellow of the IEEE and the IAPR.