Introduction to Probability Theory and Stochastic Processes - Hardcover

Chiasson, John

Hardcover

ISBN 10: 111838279X ISBN 13: 9781118382790

Publisher: Wiley, 2013

Synopsis

A unique approach to stochastic processes that connects the mathematical formulation of random processes to their use in applications

This book presents an innovative approach to teaching probability theory and stochastic processes based on the binary expansion of the unit interval. Departing from standard pedagogy, it uses the binary expansion of the unit interval to explicitly construct an infinite sequence of independent random variables (of any given distribution) on a single probability space. This construction then provides the framework to understand the mathematical formulation of probability theory for its use in applications.

Features include:

• The theory is presented first for countable sample spaces (Chapters 1-3) and then for uncountable sample spaces (Chapters 4-18)
• Coverage of the explicit construction of i.i.d. random variables on a single probability space to explain why it is the distribution function rather than the functional form of random variables that matters when it comes to modeling random phenomena
• Explicit construction of continuous random variables to facilitate the "digestion" of random variables, i.e., how they are used in contrast to how they are defined
• Explicit construction of continuous random variables to facilitate the two views of expectation: as integration over the underlying probability space (abstract view) or as integration using the density function (usual view)
• A discussion of the connections between Bernoulli, geometric, and Poisson processes
• Incorporation of the Johnson-Nyquist noise model and an explanation of why (and when) it is valid to use a delta function to model its autocovariance

Comprehensive, astute, and practical, Introduction to Probability Theory and Stochastic Processes is a clear presentation of essential topics for those studying communications, control, machine learning, digital signal processing, computer networks, pattern recognition, image processing, and coding theory.

About this title

About the Author

JOHN CHIASSON, PhD, is a Fellow of the IEEE and the author of Modeling and High-Performance Control of Electric Machines, published by Wiley-IEEE Press.

From the Back Cover

Introduction to Probability Theory and Stochastic Processes

This comprehensive textbook provides an introduction to statistical methods for graduate engineers―offering thorough coverage of important probability-related topics to aid in product and system design, reliability engineering, quality control, and more. It introduces engineers to abstract concepts in mathematical stochastic processes and probability theory and covers topics such as coin tossing, simulation of random phenomena, brownian motion, white noise, and kalman filtering.

From the Inside Flap

A unique approach to stochastic processes that connects the mathematical formulation of random processes to their use in applications

This book presents an innovative approach to teaching probability theory and stochastic processes based on the binary expansion of the unit interval. Departing from standard pedagogy, it uses the binary expansion of the unit interval to explicitly construct an infinite sequence of independent random variables (of any given distribution) on a single probability space. This construction then provides the framework to understand the mathematical formulation of probability theory for its use in applications.

Features include:

• The theory is presented first for countable sample spaces (Chapters 1-3) and then for uncountable sample spaces (Chapters 4-18)
• Coverage of the explicit construction of i.i.d. random variables on a single probability space to explain why it is the distribution function rather than the functional form of random variables that matters when it comes to modeling random phenomena
• Explicit construction of continuous random variables to facilitate the "digestion" of random variables, i.e., how they are used in contrast to how they are defined
• Explicit construction of continuous random variables to facilitate the two views of expectation: as integration over the underlying probability space (abstract view) or as integration using the density function (usual view)
• A discussion of the connections between Bernoulli, geometric, and Poisson processes
• Incorporation of the Johnson-Nyquist noise model and an explanation of why (and when) it is valid to use a delta function to model its autocovariance

Comprehensive, astute, and practical, Introduction to Probability Theory and Stochastic Processes is a clear presentation of essential topics for those studying communications, control, machine learning, digital signal processing, computer networks, pattern recognition, image processing, and coding theory.

Edition details

• Publisher: Wiley
• Publication date: 2013
• Language: English
• ISBN 10: 111838279X
• ISBN 13: 9781118382790
• Binding: Hardcover
• Edition number: 1
• Number of pages: 992