图书简介

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; 7-2 Relation Of Spectral Density To Fourier Transform: ; 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; 9-5 Systems That Maximize Signal-To-Noise Ratio: ; 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

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