Probability and Random Processes for Electrical Engineering, Leon-Garc…
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Summary 782
11.4 Continuous-Time Markov Chains 673
4.5 Functions of a Random Variable 174
Summary 129
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2.1 Specifying Random Experiments 21
11.5 Time-Reversed Markov Chains 686
5.2 Pairs of Discrete Random Variables 236
Problems 288
10.4 Optimum Linear Systems 605
11.2 Discrete-Time Markov Chains 650
10.7 Numerical Techniques for Processing Random Signals 628
4.2 The Probability Density Function 148
12.10 Simulation and Data Analysis of Queueing Systems 771
10.2 Response of Linear Systems to Random Signals 587
6.5 Estimation of Random Variables 332
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4.4 Important Continuous Random Variables 163
설명
Appendices
Probability and Random Processes for Electrical Engineering, Leon-Garcia, solution
CHAPTER 12 Introduction to Queueing Theory 713
12.6 M/G/1 Queueing Systems 738
and Random Walk 498
12.1 The Elements of a Queueing System 714
Problems 80
8.5 Hypothesis Testing 441
Probability and Random Processes for Electrical Engineering, Leon-Garcia, solution 랜덤프로세스 solution(솔루션)
9.6 Stationary Random Processes 518
4.7 Transform Methods 184
and Computer Engineering 1
8.2 Parameter Estimation 415
CHAPTER 5 Pairs of Random Variables 233
11.1 Markov Processes 647
Summary 346
Summary 213
12.9 Networks of Queues: Jackson’s Theorem 758
6.4 Jointly Gaussian Random Vectors 325
2.2 The Axioms of Probability 30
7.6 Calculating Distribution’s Using the Discrete Fourier
4.10 Entropy 202
CHAPTER 7 Sums of Random Variables and Long-Term Averages 359
Problems 18
Probability and Random Processes for Electrical Engineering, Leon-Garcia, solution 이에 대한 솔루션입니다. 밑에는 목차를 올렸습니다. 12.4 Multi-Server Systems: M/M/c, M/M/c/c,And 727
5.6 Joint Moments and Expected Values of a Function of Two Random
CHAPTER 11 Markov Chains 647
Summary 286
Problems 402
8.3 Maximum Likelihood Estimation 419
CHAPTER 2 Basic Concepts of Probability Theory 21
CHAPTER 4 One Random Variable 141
9.8 Time Averages of Random Processes and Ergodic Theorems 540
6.6 Generating Correlated Vector Random Variables 342
12.7 M/G/1 Analysis Using Embedded Markov Chains 745
6.1 Vector Random Variables 303
Probabilities 660
CHAPTER 1 Probability Models in Electrical
10.6 Estimating the Power Spectral Density 622
9.1 Definition of a Random Process 488
이에 대한 solution(솔루션)입니다.
1.2 Deterministic Models 4
7.4 Convergence of Sequences of Random Variables 378
CHAPTER 6 Vector Random Variables 303
CHAPTER 10 Analysis and Processing of Random Signals 577
5.4 The Joint pdf of Two Continuous Random Variables 248
10.1 Power Spectral Density 577
Problems 348
2.5 Independence of Events 53
3.5 Important Discrete Random Variables 115
Problems 784
Transform 392
A. Mathematical Tables 797
7.5 Long-Term Arrival Rates and Associated Averages 387
B. Tables of Fourier Transforms 800
2.9 Fine Points: Probabilities of Sequences of Events 75
C. Matrices and Linear Algebra
9.7 Continuity, Derivatives, and Integrals of Random Processes 529
Summary 554
12.3 The M/M/1 Queue 718
밑에는 목차를 올렸습니다. 제가 실제로 참고하여 공부한 솔루션이기 때문에 목차가 맞다면 이용하시면 될 것 같습니다.
확률, 통계
Summary 469
순서
8.7 Testing the Fit of a Distribution to Data 462
and Brownian Motion 514
Central Limit Theorem 370
Summary 633
3.3 Expected Value and Moments of Discrete Random Variable 104
3.6 Generation of Discrete Random Variables 127
2.7 Synthesizing Randomness: Random Number Generators 67
9.3 Discrete-Time Processes: Sum Process, Binomial Counting Process,
8.1 Samples and Sampling Distributions 411
5.5 Independence of Two Random Variables 254
2.6 Sequential Experiments 59
7.2 The Sample Mean and the Laws of Large Numbers 365
Problems 557
1.1 Mathematical Models as Tools in Analysis and Design 2
Variables 257
Problems 635
10.3 Bandlimited Random Processes 597
8.6 Bayesian Decision Methods 455
Problems 471
7.3 The Central Limit Theorem 369
3.1 The Notion of a Random Variable 96
4.8 Basic Reliability Calculations 189
3.4 Conditional Probability Mass Function 111
3.2 Discrete Random Variables and Probability Mass Function 99
2.8 Fine Points: Event Classes 70
4.6 The Markov and Chebyshev Inequalities 181
레포트 > 공학,기술계열
CHAPTER 9 Random Processes 487
11.3 Classes of States, Recurrence Properties, and Limiting
4.3 The Expected Value of X 155
5.10 Generating Independent Gaussian Random Variables 284
12.8 Burke’s Theorem: Departures From M/M/c Systems 754
9.9 Fourier Series and Karhunen-Loeve Expansion 544
12.2 Little’s Formula 715
8.4 Confidence Intervals 430
1.3 Probability Models 4
5.7 Conditional Probability and Conditional Expectation 261
6.3 Expected Values of Vector Random Variables 318
5.9 Pairs of Jointly Gaussian Random Variables 278
4.1 The Cumulative Distribution Function 141
12.5 Finite-Source Queueing Systems 734
5.3 The Joint cdf of X and Y 242
4.9 Computer Methods for Generating Random Variables 194
11.6 Numerical Techniques for Markov Chains 692
1.6 Overview of Book 16
Problems 130
Weak Law of Large Numbers 367
9.4 Poisson and Associated Random Processes 507
Problems 702
Summary 400
Strong Law of Large Numbers 368
9.5 Gaussian Random Processes,Wiener Process
5.1 Two Random Variables 233
CHAPTER 3 Discrete Random Variables 96
Summary 700
10.5 The Kalman Filter 617
7.1 Sums of Random Variables 360
CHAPTER 8 Statistics 411
1.4 A Detailed Example: A Packet Voice Transmission System 9
Problems 215
9.10 Generating Random Processes 550
5.8 Functions of Two Random Variables 271
2.4 Conditional Probability 47
6.2 Functions of Several Random Variables 309
2.3 Computing Probabilities Using Counting Methods 41
1.5 Other Examples 11
Summary 79
Summary 17
9.2 Specifying a Random Process 491
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