Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers

by ;
Format: Hardcover
Pub. Date: 1999-01-01
Publisher(s): Wiley
List Price: $125.00

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Summary

What Does Winning the Lottery Have To do with Engineering? Whether you're trying to win millions in the lottery or designing a complex computer network, you're applying probability theory. Although you encounter probability applications everywhere, the theory can be deceptively difficult to learn and apply correctly. This text will help you grasp the concepts of probability and stochastic processes and apply them throughout your careers. These concepts are clearly presented throughout the book as a sequence of building blocks that are clearly identified as either an axiom, definition, or theorem. This approach provides you with a better understanding of the material which you'll be able to use to solve practical problems. Key Features: * The text follows a single model that begins with an experiment consisting of a procedure and observations. * The mathematics of discrete random variables appears separately from the mathematics of continuous random variables. * Stochastic processes are introduced in Chapter 6, immediately after the presentation of discrete and continuous random variables. Subsequent material, including central limit theorem approximations, laws of large numbers, and statistical inference, then use examples that reinforce stochastic process concepts. * An abundance of exercises are provided that help students learn how to put the theory to use.

Table of Contents

CHAPTER 1 EXPERIMENTS, MODELS, AND PROBABILITIES
1(42)
Getting Started with Probability 1(2)
1.1 Set Theory
3(4)
1.2 Applying Set Theory to Probability
7(5)
1.3 Probability Axioms
12(3)
1.4 Some Consequences of the Axioms
15(1)
1.5 Conditional Probability
16(5)
1.6 Independence
21(3)
1.7 Sequential Experiments and Tree Diagrams
24(3)
1.8 Counting Methods
27(4)
1.9 Independent Trials
31(5)
Chapter Summary
36(1)
Problems
36(7)
CHAPTER 2 DISCRETE RANDOM VARIABLES
43(44)
2.1 Definitions
43(3)
2.2 Probability Mass Function
46(3)
2.3 Some Useful Discrete Random Variables
49(6)
2.4 Cumulative Distribution Function (CDF)
55(4)
2.5 Averages
59(5)
2.6 Functions of a Random Variable
64(4)
2.7 Expected Value of a Derived Random Variable
68(2)
2.8 Variance and Standard Deviation
70(4)
2.9 Conditional Probability Mass Function
74(5)
Chapter Summary
79(1)
Problems
80(7)
CHAPTER 3 MULTIPLE DISCRETE RANDOM VARIABLES
87(32)
3.1 Joint Probability Mass Function
87(3)
3.2 Marginal PMF
90(3)
3.3 Functions of Two Random Variables
93(1)
3.4 Expectations
94(6)
3.5 Conditioning a Joint PMF by an Event
100(2)
3.6 Conditional PMF
102(4)
3.7 Independent Random Variables
106(2)
3.8 More Than Two Discrete Random Variables
108(3)
Chapter Summary
111(1)
Problems
112(7)
CHAPTER 4 CONTINUOUS RANDOM VARIABLES
119(46)
Continuous Sample Space 119(2)
4.1 The Cumulative Distribution Function
121(2)
4.2 Probability Density Function
123(6)
4.3 Expected Values
129(3)
4.4 Some Useful Continuous Random Variables
132(5)
4.5 Gaussian Random Variables
137(7)
4.6 Delta Functions, Mixed Random Variables
144(6)
4.7 Probability Models of Derived Random Variables
150(5)
4.8 Conditioning a Continuous Random Variable
155(4)
Chapter Summary
159(1)
Problems
159(6)
CHAPTER 5 MULTIPLE CONTINUOUS RANDOM VARIABLES
165(36)
5.1 Joint Cumulative Distribution Function
165(2)
5.2 Joint Probability Density Function
167(5)
5.3 Marginal PDF
172(2)
5.4 Functions of Two Random Variables
174(3)
5.5 Expected Values
177(2)
5.6 Conditioning a Joint PDF by an Event
179(2)
5.7 Conditional PDF
181(3)
5.8 Independent Random Variables
184(2)
5.9 Jointly Gaussian Random Variables
186(5)
5.10 More Than Two Continuous Random Variables
191(4)
Chapter Summary
195(1)
Problems
196(5)
CHAPTER 6 STOCHASTIC PROCESSES
201(30)
Definitions 201(2)
6.1 Stochastic Process Examples
203(2)
6.2 Types of Stochastic Processes
205(2)
6.3 Random Variables from Random Processes
207(3)
6.4 Independent, Identically Distributed Random Sequences
210(1)
6.5 The Poisson Process
211(4)
6.6 The Brownian Motion Process
215(1)
6.7 Expected Value and Correlation
216(3)
6.8 Stationary Processes
219(4)
6.9 Wide Sense Stationary Random Processes
223(2)
Chapter Summary
225(1)
Problems
226(5)
CHAPTER 7 SUMS OF RANDOM VARIABLES
231(30)
7.1 Expectations of Sums
231(4)
7.2 PDF of the Sum of Two Random Variables
235(1)
7.3 Moment Generating Function
236(4)
7.4 MGF of the Sum of Independent Random Variables
240(2)
7.5 Sums of Independent Gaussian Random Variables
242(2)
7.6 Random Sums of Independent Random Variables
244(3)
7.7 Central Limit Theorem
247(5)
7.8 Applications of the Central Limit Theorem
252(3)
Chapter Summary
255(1)
Problems
256(5)
CHAPTER 8 THE SAMPLE MEAN
261(18)
8.1 Expected Value and Variance
261(2)
8.2 Useful Inequalities
263(3)
8.3 Sample Mean of Large Numbers
266(3)
8.4 Laws of Large Numbers
269(6)
Chapter Summary
275(1)
Problems
276(3)
CHAPTER 9 STATISTICAL INFERENCE
279(44)
9.1 Significance Testing
281(2)
9.2 Binary Hypothesis Testing
283(9)
9.3 Multiple Hypothesis Test
292(3)
9.4 Estimation of a Random Variable
295(5)
9.5 Linear Estimation of X given Y
300(7)
9.6 MAP and ML Estimation
307(3)
9.7 Estimation of Model Parameters
310(6)
Chapter Summary
316(1)
Problems
317(6)
CHAPTER 10 RANDOM SIGNAL PROCESSING
323(22)
10.1 Linear Filtering of a Random Process
323(4)
10.2 Power Spectral Density
327(3)
10.3 Cross Correlations
330(5)
10.4 Gaussian Processes
335(3)
10.5 White Gaussian Noise Processes
338(2)
10.6 Digital Signal Processing
340(1)
Chapter Summary
341(1)
Problems
342(3)
CHAPTER 11 RENEWAL PROCESSES AND MARKOV CHAINS
345(52)
11.1 Renewal Processes
345(6)
11.2 Poisson Process
351(4)
11.3 Renewal-Reward Processes
355(2)
11.4 Discrete Time Markov Chains
357(3)
11.5 Discrete Time Markov Chain Dynamics
360(3)
11.6 Limiting State Probabilities
363(4)
11.7 State Classification
367(6)
11.8 Limit Theorems For Discrete Time Markov Chains
373(4)
11.9 Periodic States and Multiple Communicating Classes
377(4)
11.10 Continuous Time Markov Chains
381(5)
11.11 Birth-Death Processes and Queueing Systems
386(5)
Chapter Summary
391(1)
Problems
392(5)
APPENDIX A COMMON RANDOM VARIABLES 397(6)
A.1 Discrete Random Variables 397(2)
A.2 Continuous Random Variables 399(4)
APPENDIX B QUIZ SOLUTIONS 403(46)
Quiz Solutions--Chapter 1 403(4)
Quiz Solutions--Chapter 2 407(5)
Quiz Solutions--Chapter 3 412(7)
Quiz Solutions--Chapter 4 419(4)
Quiz Solutions--Chapter 5 423(5)
Quiz Solutions--Chapter 6 428(3)
Quiz Solutions--Chapter 7 431(4)
Quiz Solutions--Chapter 8 435(2)
Quiz Solutions--Chapter 9 437(4)
Quiz Solutions--Chapter 10 441(3)
Quiz Solutions--Chapter 11 444(5)
REFERENCES 449(1)
INDEX 450

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