Probabilistic Methods of Signal and System Analysis
Sorozatcím: The Oxford Series in Electrical and Computer Engineering;
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A termék adatai:
- Kiadás sorszáma 3
- Kiadó OUP USA
- Megjelenés dátuma 1998. szeptember 10.
- ISBN 9780195123548
- Kötéstípus Keménykötés
- Terjedelem496 oldal
- Méret 242x196x30 mm
- Súly 998 g
- Nyelv angol
- Illusztrációk numerous line figures 0
Kategóriák
Rövid leírás:
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.
TöbbHosszú leírás:
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.
TöbbTartalomjegyzék:
Preface
Introduction To Probability
Engineering Applications Of Probability
Random Experiments And Events
Definitions Of Probability
The Relative-Frequency Approach
Elementary Set Theory
The Axiomatic Approach
Conditional Probability
Independence
Combined Experiments
Bemoulli Trials
Applications Of Bemoulli Trials
Random Variables
Concept Of A Random Variable
Distribution Functions
Density Functions
Mean Values And Moments
The Gaussian Random Variable
Density Functions Related To Gaussian
Other Probability Density Functions
Conditional Probability Distribution And Density Functions
Examples And Applications
Several Random Variables
Two Random Variables
Conditional Probability-Revisited
Statistical Independence
Correlation Between Random Variables
Density Function Of The Sum Of Two Random Variables
Probability Density Function Of A Function Of Two Random Variables
The Characteristic Function
Elements oOf Statistics
Introduction
Sampling Theory- The Sample Mean
Sampling Theory- The Sample Variance
Sampling Distributions And Confidence Intervals
Hypothesis Testing
Curve Fitting And Linear Regression
Correlation Between Two Sets of Data
Random Processes
Introduction
Continuous And Discrete Random Processes
Deterministic And Nondeterministic Random Processes
Stationary and Nonstationary Random Processes
Ergodic And Nonergodic Random Processes
Measurement Of Process Parameters
Smoothing Data With A Moving Window Average
Correlation Functions
Introduction
Example:Autocorrelation Function Of A Binary Profess
Properties Of Autocorrelation Functions
Measurement Of Autocorrelation Functions
Examples Of Autocorrelation Functions
Crosscorrelation Functions
Properties Of Crosscorrelation Functions
Examples And Applications Of Crosscorrelation Functions
Correlation Matrices For Sampled Functions
Spectral Density
Introduction
Properties Of Spectral Density
Spectral Density And The Complex Frequency Plane
Mean-Square Values From Spectral Density
Relation Of Spectral Density To The Autocorrelation Function
White Noise
Cross-Spectral Density
Measurement Of Spectral Density
Periodogram Estimate Of Spectral Density
Examples And Applications Of Spectral Density
Repines Of Linear Systems To Random Inputs
Introduction
Analysis In The Time Domain
Mean And Mean-Swquare Value Of System Output
Autocorrelation Function Of System Output
Crosscorrelation Between Input And Output
Example Of Time-Domain Analysis
Analysis In The Frequency Domain
Spectral Density At The System Output
Cross-Spectral Densities Between Input And Output
Examples Of Frequency-Domain Analysis
Numerical Computation Of System Output
Optimum Linear Systems
Introduction
Criteria Of Optimaility
Restrictions On The Optimum System
Optimization By Parameter Adjustment
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
