Name: Probability & Statistics for Electrical Engineering
Brand: Ouvrages de référence
Price: 15.00 USD
Availability: InStock

Third edition textbook covering probability theory, statistics, and random processes with applications to electrical and computer engineering. Includes chapters on basic probability concepts, discrete and continuous random variables, random processes, Markov chains, queueing theory, and signal processing. Features MATLAB/Octave exercises, practical engineering examples in wireless communications, digital signal processing, and system reliability.

Table of Contents:

Preface .... page ix
CHAPTER 1 Probability Models in Electrical and Computer Engineering .... page 1
1.1 Mathematical Models as Tools in Analysis and Design .... page 2
1.2 Deterministic Models .... page 4
1.3 Probability Models .... page 4
1.4 A Detailed Example: A Packet Voice Transmission System .... page 9
1.5 Other Examples .... page 11
1.6 Overview of Book .... page 16
Summary .... page 17
Problems .... page 18
CHAPTER 2 Basic Concepts of Probability Theory .... page 21
2.1 Specifying Random Experiments .... page 21
2.2 The Axioms of Probability .... page 30
*2.3 Computing Probabilities Using Counting Methods .... page 41
2.4 Conditional Probability .... page 47
2.5 Independence of Events .... page 53
2.6 Sequential Experiments .... page 59
*2.7 Synthesizing Randomness: Random Number Generators .... page 67
*2.8 Fine Points: Event Classes .... page 70
*2.9 Fine Points: Probabilities of Sequences of Events .... page 75
Summary .... page 79
Problems .... page 80
CHAPTER 3 Discrete Random Variables .... page 96
3.1 The Notion of a Random Variable .... page 96
3.2 Discrete Random Variables and Probability Mass Function .... page 99
3.3 Expected Value and Moments of Discrete Random Variable .... page 104
3.4 Conditional Probability Mass Function .... page 111
3.5 Important Discrete Random Variables .... page 115
3.6 Generation of Discrete Random Variables .... page 127
Summary .... page 129
Problems .... page 130
CHAPTER 4 One Random Variable .... page 141
4.1 The Cumulative Distribution Function .... page 141
4.2 The Probability Density Function .... page 148
4.3 The Expected Value of X .... page 155
4.4 Important Continuous Random Variables .... page 163
4.5 Functions of a Random Variable .... page 174
4.6 The Markov and Chebyshev Inequalities .... page 181
4.7 Transform Methods .... page 184
4.8 Basic Reliability Calculations .... page 189
4.9 Computer Methods for Generating Random Variables .... page 194
*4.10 Entropy .... page 202
Summary .... page 213
Problems .... page 215
CHAPTER 5 Pairs of Random Variables .... page 233
5.1 Two Random Variables .... page 233
5.2 Pairs of Discrete Random Variables .... page 236
5.3 The Joint cdf of X and Y .... page 242
5.4 The Joint pdf of Two Continuous Random Variables .... page 248
5.5 Independence of Two Random Variables .... page 254
5.6 Joint Moments and Expected Values of a Function of Two Random Variables .... page 257
5.7 Conditional Probability and Conditional Expectation .... page 261
5.8 Functions of Two Random Variables .... page 271
5.9 Pairs of Jointly Gaussian Random Variables .... page 278
5.10 Generating Independent Gaussian Random Variables .... page 284
Summary .... page 286
Problems .... page 288
CHAPTER 6 Vector Random Variables .... page 303
6.1 Vector Random Variables .... page 303
6.2 Functions of Several Random Variables .... page 309
6.3 Expected Values of Vector Random Variables .... page 318
6.4 Jointly Gaussian Random Vectors .... page 325
6.5 Estimation of Random Variables .... page 332
6.6 Generating Correlated Vector Random Variables .... page 342
Summary .... page 346
Problems .... page 348
CHAPTER 7 Sums of Random Variables and Long-Term Averages .... page 359
7.1 Sums of Random Variables .... page 360
7.2 The Sample Mean and the Laws of Large Numbers .... page 365
Weak Law of Large Numbers .... page 367
Strong Law of Large Numbers .... page 368
7.3 The Central Limit Theorem .... page 369
Central Limit Theorem .... page 370
*7.4 Convergence of Sequences of Random Variables .... page 378
*7.5 Long-Term Arrival Rates and Associated Averages .... page 387
7.6 Calculating Distribution's Using the Discrete Fourier Transform .... page 392
Summary .... page 400
Problems .... page 402
CHAPTER 8 Statistics .... page 411
8.1 Samples and Sampling Distributions .... page 411
8.2 Parameter Estimation .... page 415
8.3 Maximum Likelihood Estimation .... page 419
8.4 Confidence Intervals .... page 430
8.5 Hypothesis Testing .... page 441
8.6 Bayesian Decision Methods .... page 455
8.7 Testing the Fit of a Distribution to Data .... page 462
Summary .... page 469
Problems .... page 471
CHAPTER 9 Random Processes .... page 487
9.1 Definition of a Random Process .... page 488
9.2 Specifying a Random Process .... page 491
9.3 Discrete-Time Processes: Sum Process, Binomial Counting Process, and Random Walk .... page 498
9.4 Poisson and Associated Random Processes .... page 507
9.5 Gaussian Random Processes, Wiener Process and Brownian Motion .... page 514
9.6 Stationary Random Processes .... page 518
9.7 Continuity, Derivatives, and Integrals of Random Processes .... page 529
9.8 Time Averages of Random Processes and Ergodic Theorems .... page 540
*9.9 Fourier Series and Karhunen-Loeve Expansion .... page 544
9.10 Generating Random Processes .... page 550
Summary .... page 554
Problems .... page 557
CHAPTER 10 Analysis and Processing of Random Signals .... page 577
10.1 Power Spectral Density .... page 577
10.2 Response of Linear Systems to Random Signals .... page 587
10.3 Bandlimited Random Processes .... page 597
10.4 Optimum Linear Systems .... page 605
*10.5 The Kalman Filter .... page 617
*10.6 Estimating the Power Spectral Density .... page 622
10.7 Numerical Techniques for Processing Random Signals .... page 628
Summary .... page 633
Problems .... page 635
CHAPTER 11 Markov Chains .... page 647
11.1 Markov Processes .... page 647
11.2 Discrete-Time Markov Chains .... page 650
11.3 Classes of States, Recurrence Properties, and Limiting Probabilities .... page 660
11.4 Continuous-Time Markov Chains .... page 673
*11.5 Time-Reversed Markov Chains .... page 686
11.6 Numerical Techniques for Markov Chains .... page

Probability & Statistics for Electrical Engineering

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