Probabilistic Methods of Signal and System Analysis

3rd Auflage
ISBN: 978-0-19-512354-8
Verlag: Oxford University Press

Buch, Englisch, 496 Seiten, Format (B × H): 196 mm x 241 mm, Gewicht: 1115 g

Since its original publication in 1971, this text has been a standard for signals and systems courses that emphasize probability. It provides an introduction to probability theory, statistics, random processes, and the analysis of systems with random inputs. The third edition will utilize MATLAB as a computational tool. It will be thoroughly revised to include new examples and problems, and updated to reflect the most current research and technologies. This book is intended for the junior/senior level engineering students.

Autoren/Hrsg.

Cooper, George R.

McGillem, Clare D.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
• Geisteswissenschaften Design Produktdesign, Industriedesign
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

- Preface

- 1: Introduction To Probability

- 1-1: Engineering Applications Of Probability

- 1-2: Random Experiments And Events

- 1-3: Definitions Of Probability

- 1-4: The Relative-Frequency Approach

- 1-5: Elementary Set Theory

- 1-6: The Axiomatic Approach

- 1-7: Conditional Probability

- 1-8: Independence

- 1-9: Combined Experiments

- 1-10: Bemoulli Trials

- 1-11: Applications Of Bemoulli Trials

- 2: Random Variables

- 2-1: Concept Of A Random Variable

- 2-2: Distribution Functions

- 2-3: Density Functions

- 2-4: Mean Values And Moments

- 2-5: The Gaussian Random Variable

- 2-6: Density Functions Related To Gaussian

- 2-7: Other Probability Density Functions

- 2-8: Conditional Probability Distribution And Density Functions

- 2-9: Examples And Applications

- 3: Several Random Variables

- 3-1: Two Random Variables

- 3-2: Conditional Probability-Revisited

- 3-3: Statistical Independence

- 3-4: Correlation Between Random Variables

- 3-5: Density Function Of The Sum Of Two Random Variables

- 3-6: Probability Density Function Of A Function Of Two Random Variables

- 3-7: The Characteristic Function

- 4: Elements oOf Statistics

- 4-1: Introduction

- 4-2: Sampling Theory- The Sample Mean

- 4-3: Sampling Theory- The Sample Variance

- 4-4: Sampling Distributions And Confidence Intervals

- 4-5: Hypothesis Testing

- 4-6: Curve Fitting And Linear Regression

- 4-7: Correlation Between Two Sets of Data

- 5: Random Processes

- 5-1: Introduction

- 5-2: Continuous And Discrete Random Processes

- 5-3: Deterministic And Nondeterministic Random Processes

- 5-4: Stationary and Nonstationary Random Processes

- 5-5: Ergodic And Nonergodic Random Processes

- 5-6: Measurement Of Process Parameters

- 5-7: Smoothing Data With A Moving Window Average

- 6: Correlation Functions

- 6-1: Introduction

- 6-2: Example:Autocorrelation Function Of A Binary Profess

- 6-3: Properties Of Autocorrelation Functions

- 6-4: Measurement Of Autocorrelation Functions

- 6-5: Examples Of Autocorrelation Functions

- 6-6: Crosscorrelation Functions

- 6-7: Properties Of Crosscorrelation Functions

- 6-8: Examples And Applications Of Crosscorrelation Functions

- 6-9: Correlation Matrices For Sampled Functions

- 7: Spectral Density

- 7-1: Introduction

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- 7-3: Properties Of Spectral Density

- 7-4: Spectral Density And The Complex Frequency Plane

- 7-5: Mean-Square Values From Spectral Density

- 7-6: Relation Of Spectral Density To The Autocorrelation Function

- 7-7: White Noise

- 7-8: Cross-Spectral Density

- 7-9: Measurement Of Spectral Density

- 7-10: Periodogram Estimate Of Spectral Density

- 7-11: Examples And Applications Of Spectral Density

- 8: Repines Of Linear Systems To Random Inputs

- 8-1: Introduction

- 8-2: Analysis In The Time Domain

- 8-3: Mean And Mean-Swquare Value Of System Output

- 8-4: Autocorrelation Function Of System Output

- 8-5: Crosscorrelation Between Input And Output

- 8-6: Example Of Time-Domain Analysis

- 8-7: Analysis In The Frequency Domain

- 8-8: Spectral Density At The System Output

- 8-9: Cross-Spectral Densities Between Input And Output

- 8-10: Examples Of Frequency-Domain Analysis

- 8-11: Numerical Computation Of System Output

- 9: Optimum Linear Systems

- 9-1: Introduction

- 9-2: Criteria Of Optimaility

- 9-3: Restrictions On The Optimum System

- 9-4: Optimization By Parameter Adjustment

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- 9-6: Systems That Minimize Mean-Square Error

- Appendices

- Appendix A: Mathematical Tables

- Appendix B: Frequently Encountered Probability Distributions

- Appendix C: Binomial Coefficients

- Appendix D: Normal Probability Distribution Function

- Appendix E: The Q-Function

- Appendix F: Student's T-Distribution Function

- Appendix G: Computer Computations

- Appendix H: Table Of Correlation Function-Spectral Density Pairs

- Appendix I: Contour Integration