Probability, Random Variables, and Random Processes

Probability is ubiquitous in every branch of science and engineering. This text on probability and random processes assumes basic prior knowledge of the subject at the undergraduate level.

Informationen zum Autor JOHN J. SHYNK, PhD, is Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. He was a Member of Technical Staff at Bell Laboratories, and received degrees in systems engineering, electrical engineering, and statistics from Boston University and Stanford University. Klappentext Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background.The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website.Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing. Zusammenfassung Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background.The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities.* Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute...

Autorentext
JOHN J. SHYNK, PhD, is Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. He was a Member of Technical Staff at Bell Laboratories, and received degrees in systems engineering, electrical engineering, and statistics from Boston University and Stanford University.

Klappentext

Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.

Inhalt

PREFACE xxi NOTATION xxv 1 Overview and Background 1 1.1 Introduction 1 1.2 Deterministic Signals and Systems 19 1.3 Statistical Signal Processing with MATLAB(r) 35 Problems 39 Further Reading 45 PART I Probability, Random Variables, and Expectation 2 Probability Theory 49 2.1 Introduction 49 2.2 Sets and Sample Spaces 50 2.3 Set Operations 54 2.4 Events and Fields 58 2.5 Summary of a Random Experiment 64 2.6 Measure Theory 64 2.7 Axioms of Probability 68 2.8 Basic Probability Results 69 2.9 Conditional Probability 71 2.10 Independence 73 2.11 Bayes' Formula 74 2.12 Total Probability 76 2.13 Discrete Sample Spaces 79 2.14 Continuous Sample Spaces 83 2.15 Nonmeasurable Subsets of R 84 Problems 87 Further Reading 90 3 Random Variables 91 3.1 Introduction 91 3.2 Functions and Mappings 91 3.3 Distribution Function 96 3.4 Probability Mass Function 101 3.5 Probability Density Function 103 3.6 Mixed Distributions 104 3.7 Parametric Models for Random Variables 107 3.8 Continuous Random Variables 109 3.9 Discrete Random Variables 151 Problems 173 Further Reading 176 4 Multiple Random Variables 177 4.1 Introduction 177 4.2 Random Variable Approximations 177 4.3 Joint and Marginal Distributions 183 4.4 Independent Random Variables 186 4.5 Conditional Distribution 187 4.6 Random Vectors 190 4.7 Generating Dependent…