For courses in Probability and Random Processes.
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Probability, Statistics, and Random Processes for Engineers, 4e is a comprehensive treatment of probability and random processes that, more than any other available source, combines
rigor with
accessibility. Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences, continuous-time random processes, and statistical signal processing. The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms?and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes.
Preface 1 Introduction to Probability 1 1.1 Introduction: Why Study Probability? 1 1.2 The Different Kinds of Probability 2 Probability as Intuition 2 Probability as the Ratio of Favorable to Total Outcomes (Classical Theory) 3 Probability as a Measure of Frequency of Occurrence 4 Probability Based on an Axiomatic Theory 5 1.3 Misuses, Miscalculations, and Paradoxes in Probability 7 1.4 Sets, Fields, and Events 8 Examples of Sample Spaces 8 1.5 Axiomatic Definition of Probability 15 1.6 Joint, Conditional, and Total Probabilities; Independence 20 Compound Experiments 23 1.7 Bayes Theorem and Applications 35 1.8 Combinatorics 38 Occupancy Problems 42 Extensions and Applications 46 1.9 Bernoulli Trials--Binomial and Multinomial Probability Laws 48 Multinomial Probability Law 54 1.10 Asymptotic Behavior of the Binomial Law: The Poisson Law 57 1.11 Normal Approximation to the Binomial Law 63 Summary 65 Problems 66 References 77 2 Random Variables 79 2.1 Introduction 79 2.2 Definition of a Random Variable 80 2.3 Cumulative Distribution Function 83 Properties of FX(x) 84 Computation of FX(x) 85 2.4 Probability Density Function (pdf) 88 Four Other Common Density Functions 95 More Advanced Density Functions 97 2.5 Continuous, Discrete, and Mixed Random Variables 100 Some Common Discrete Random Variables 102 2.6 Conditional and Joint Distributions and Densities 107 Properties of Joint CDF FXY (x, y) 118 2.7 Failure Rates 137 Summary 141 Problems 141 References 149 Additional Reading 149 3 Functions of Random Variables 151 3.1 Introduction 151 Functions of a Random Variable (FRV): Several Views 154 3.2 Solving Problems of the Type Y = g(X) 155 General Formula of Determining the pdf of Y = g(X) 166 3.3 Solving Problems of the Type Z = g(X, Y ) 171 3.4 Solving Problems of the Type V = g(X, Y ), W = h(X, Y ) 193 Fundamental Problem 193 Obtaining fVW Directly from fXY 196 3.5 Additional Examples 200 Summary 205 Problems 206 References 214 Additional Reading 214 4 Expectation and Moments 215 4.1 Expected Value of a Random Variable 215 On the Validity of Equation 4.1-8 218 4.2 Conditional Expectations 232 Conditional Expectation as a Random Variable 239 4.3 Moments of Random Variables 242 Joint Moments 246 Properties of Uncorrelated Random Variables 248 Jointly Gaussian Random Variables 251 4.4 Chebyshev and Schwarz Inequalities 255 Markov Inequality 257 The Schwarz Inequality 258 4.5 Moment-Generating Functions 261 4.6 Chernoff Bound 264 4.7 Characteristic Functions 266 Joint Characteristic Functions 273 The Central Limit Theorem 276 4.8 Additional Examples 281 Summary 283 Problems 284 References 293 Additional Reading 294 5 Random Vectors 295 5.1 Joint Distribution and Densities 295 5.2 Multiple Transformation of Random Variables 299 5.3 Ordered Random Variables 302 Distribution of area random variables 305 5.4 Expectation Vectors and Covariance Matrices 311 5.5 Properties of Covariance Matrices 314 Whitening Transformation 318 5.6 The Multidimensional Gaussian (Normal) Law 319 5.7 Characteristic Functions of Random Vectors 328 Properties of CF of Random Vectors 330 The Characteristic Function of the Gaussian (Normal) Law 331 Summary 332 Problems 333 References 339 Additional Reading 339 6 Statistics: Part 1 Parameter Estimation 340 6.1 Introduction 340 Independent, Identically Distributed (i.i.d.) Observations 341 Estimation of Probabilities 343 6.2 Estimators 346 6.3 Estimation of the Mean 348 Properties of the Mean-Estimator Function (MEF) 349 Procedure for Getting a d-confidence Interval on the Mean of a Normal Random Variable When sX Is Known 352 Confidence Interval for the Mean of a Normal Distribution When sX Is Not Known 352 Procedure for Getting a d-Confidence Interval Based on n Observations on the Mean of a Normal Random Variable when sX Is Not Known 355 Interpretation of the Confidence Interval 355 6.4 Estimation of the Variance and Covariance 355 Confidence Interval for the Variance of a Normal Random variable 357 Estimating the Standard Deviation Directly 359 Estimating the covariance 360 6.5 Simultaneous Estimation of Mean and Variance 361 6.6 Estimation of Non-Gaussian Parameters from Large Samples 363 6.7 Maximum Likelihood Estimators 365 6.8 Ordering, more on Percentiles, Parametric Versus Nonparametric Statistics 369 The Median of a Population Versus Its Mean 371 Parametric versus Nonparametric Statistics 372 Confidence Interval on the Percentile 373 Confidence Interval for the Median When n Is Large 375 6.9 Estimation of Vector Means and Covariance Matrices 376 Estimation of 377 Estimation of the covariance K 378 6.10 Linear Estimation of Vector Parameters 380 Summary 384 Problems 384 References 388 Additional Reading 389 7 Statistics: Part 2 Hypothesis Testing 390 7.1 Bayesian Decision Theory 391 7.2 Likelihood Ratio Test 396 7.3 Composite Hypotheses 402 Generalized Likelihood Ratio Test (GLRT) 403 How Do We Test for the Equality of Means of Two Populations? 408 Testing for the Equality of Variances for Normal Populations: The F-test 412 Testing Whether the Variance of a Normal Population Has a Predetermined Value: 416 7.4 Goodness of Fit 417 7.5 Ordering, Percentiles, and Rank 423 How Ordering is Useful in Estimating Percentiles and the Median 425 Confidence Interval for the Median When n Is Large 428 Distribution-free Hypothesis Testing: Testing If Two Population are the Same Using Runs 429 Ranking Test for Sameness of Two Populations 432 Summary 433 Problems 433 References 439 8 Random Sequences 441 8.1 Basic Concepts 442 Infinite-length Bernoulli Trials 447 Continuity of Probability Measure 452 Statistical Specification of a Random Sequence 454 8.2 Basic Principles of Discrete-Time Linear Systems 471 8.3 Random Sequences and Linear Systems 477 8.4 WSS Random Sequences 486 Power Spectral Density 489 Interpretation of the psd 490 Synthesis of Random Sequences and Discrete-Time Simulation 493 Decimation 496 Interpolation 497 8.5 Markov Random Sequences 500 ARMA Models 503 Markov Chains 504 8.6 Vector Random Sequences and State Equations 511 8.7 Convergence of Random Sequences 513 8.8 Laws of Large Numbers 521 Summary 526 Problems 526 References 541 9 Random Processes 543 9.1 Basic Definitions 544 9.2 Some Important Random Processes 548 Asynchronous Binary Signaling 548 Poisson Counting Process 550 Alternative Derivation of Poisson Process 555 Random Telegraph Digital Modulation Using Phase-Shift Keying 558 Wiener Process or Brownian Motion 560 Markov Random Processes 563 Birth--Death Markov Chains 567 Chapman--Kolmogorov Equations 571 Random Process Generated from Random Sequences 572 9.3 Continuous-Time Linear Systems with Random Inputs 572 White Noise 577 9.4 Some Useful Classifications of Random Processes 578 Stationarity 579 9.5 Wide-Sense Stationary Processes and LSI Systems 581 Wide-Sense Stationary Case 582 Power Spectral Density 584 An Interpretation of the psd 586 More on White Noise 590 Stationary Processes and Differential Equations 596 9.6 Periodic and Cyclostationary Processes 600 9.7 Vector Processes and State Equations 606 State Equations 608 Summary 611 Problems 611 References 633 Chapters 10 and 11 are available as Web chapters on the companion Web site at http://www.pearsonhighered.com/stark. 10 Advanced Topics in Random Processes 635 10.1 Mean-Square (m.s.) Calculus 635 Stochastic Continuity and Derivatives [10-1] 635 Further Results on m.s. Convergence [10-1] 645 10.2 Mean-Square Stochastic Integrals 650 10.3 Mean-Square Stochastic Differential Equations 653 10.4 Ergodicity [10-3] 658 10.5 Karhunen--Loeve Expansion [10-5] 665 10.6 Representation of Bandlimited and Periodic Processes 671 Bandlimited Processes 671 Bandpass Random Processes 674 WSS Periodic Processes 677 Fourier Series for WSS Processes 680 Summary 682 Appendix: Integral Equations 682 Existence Theorem 683 Problems 686 References 699 11 Applications to Statistical Signal Processing 700 11.1 Estimation of Random Variables and Vectors 700 More on the Conditional Mean 706 Orthogonality and Linear Estimation 708 Some Properties of the Operator E 716 11.2 Innovation Sequences and Kalman Filtering 718 Predicting Gaussian Random Sequences 722 Kalman Predictor and Filter 724 Error-Covariance Equations 729 11.3 Wiener Filters for Random Sequences 733 Unrealizable Case (Smoothing) 734 Causal Wiener Filter 736 11.4 Expectation-Maximization Algorithm 738 Log-likelihood for the Linear Transformation 740 Summary of the E-M algorithm 742 E-M Algorithm for Exponential Probability Functions 743 Application to Emission Tomography 744 Log-likelihood Function of Complete Data 746 E-step 747 M-step 748 11.5 Hidden Markov Models (HMM) 749 Specification of an HMM 751 Application to Speech Processing 753 Efficient Computation of P[E|M] with a Recursive Algorithm 754 Viterbi Algorithm and the Most Likely State Sequence for the Observations 756 11.6 Spectral Estimation 759 The Periodogram 760 Bartletts Procedure---Averaging Periodograms 762 Parametric Spectral Estimate 767 Maximum Entropy Spectral Density 769 11.7 Simulated Annealing 772 Gibbs Sampler 773 Noncausal Gauss--Markov Models 774 Compound Markov Models 778 Gibbs Line Sequence 779 Summary 783 Problems 783 References 788 Appendix A Review of Relevant Mathematics A-1 A.1 Basic Mathematics A-1 Sequences A-1 Convergence A-2 Summations A-3 Z-Transform A-3 A.2 Continuous Mathematics A-4 Definite and Indefinite Integrals A-5 Differentiation of Integrals A-6 Integration by Parts A-7 Completing the Square A-7 Double Integration A-8 Functions A-8 A.3 Residue Method for Inverse Fourier Transformation A-10 Fact A-11 Inverse Fourier Transform for psd of Random Sequence A-13 A.4 Mathematical Induction A-17 References A-17 Appendix B Gamma and Delta Functions B-1 B.1 Gamma Function B-1 B.2 Incomplete Gamma Function B-2 B.3 Dirac Delta Function B-2 References B-5 Appendix C Functional Transformations and Jacobians C-1 C.1 Introduction C-1 C.2 Jacobians for n = 2 C-2 C.3 Jacobian for General n C-4 Appendix D Measure and Probability D-1 D.1 Introduction and Basic Ideas D-1 Measurable Mappings and Functions D-3 D.2 Application of Measure Theory to Probability D-3 Distribution Measure D-4 Appendix E Sampled Analog Waveforms and Discrete-time Signals E-1 Appendix F Independence of Sample Mean and Variance for Normal Random Variables F-1 Appendix G Tables of Cumulative Distribution Functions: the Normal, Student t, Chi-square, and F G-1 Index I-1