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A First Course in Probability, Eighth Edition

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Combinatorial Analysis

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A First Course in Probability, Eighth Edition

by Sheldon Ross

Publisher: Pearson

Copyright Year: 2010

Publishing Date: 2008/12/28

eText ISBN-10: 0-321-60014-2

eText ISBN-13: 978-0-321-60014-1

Print ISBN-10: 0-13-603313-X

Print ISBN-13: 978-0-13-603313-4

Course: Intermediate/Advanced Statistics

Pages: 552

eTextbook $51.20

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Table of Contents | Description

Instructor Resources | Student Resources

ABOUT THIS TITLE - TABLE OF CONTENTS

1. Combinatorial Analysis, 1

1.1. Introduction, 1

1.2. The Basic Principle of Counting, 1

1.3. Permutations, 3

1.4. Combinations, 5

1.5. Multinomial Coefficients, 9

1.6. The Number of Integer Solutions of Equations, 12

Theoretical Exercises, 18

Self-Test Problems and Exercises, 20

2. Axioms of Probability, 22

2.1. Introduction, 22

2.2. Sample Space and Events, 22

2.3. Axioms of Probability, 26

2.4. Some Simple Propositions, 29

2.5. Sample Spaces Having Equally Likely Outcomes, 33

2.6. Probability as a Continuous Set Function, 44

2.7. Probability as a Measure of Belief, 48

Theoretical Exercises, 54

Self-Test Problems and Exercises, 56

3. Conditional Probability and Independence, 58

3.1. Introduction, 58

3.2. Conditional Probabilities, 58

3.3. Bayes’s Formula, 65

3.4. Independent Events, 79

3.5. P(·|F) Is a Probability, 93

Theoretical Exercises, 110

Self-Test Problems and Exercises, 114

4. Random Variables, 117

4.1. Random Variables, 117

4.2. Discrete Random Variables, 123

4.3. Expected Value, 125

4.4. Expectation of a Function of a Random Variable, 128

4.5. Variance, 132

4.6. The Bernoulli and Binomial Random Variables, 134

4.6.1. Properties of Binomial Random Variables, 139

4.6.2. Computing the Binomial Distribution Function, 142

4.7. The Poisson Random Variable, 143

4.7.1. Computing the Poisson Distribution Function, 154

4.8. Other Discrete Probability Distributions, 155

4.8.1. The Geometric Random Variable, 155

4.8.2. The Negative Binomial Random Variable, 157

4.8.3. The Hypergeometric Random Variable, 160

4.8.4. The Zeta (or Zipf) Distribution, 163

4.9. Expected Value of Sums of Random Variables, 164

4.10. Properties of the Cumulative Distribution Function, 168

Theoretical Exercises, 179

Self-Test Problems and Exercises, 183

5. Continuous Random Variables, 186

5.1. Introduction, 186

5.2. Expectation and Variance of Continuous Random Variables, 190

5.3. The Uniform Random Variable, 194

5.4. Normal Random Variables, 198

5.4.1. The Normal Approximation to the Binomial Distribution, 204

5.5. Exponential Random Variables, 208

5.5.1. Hazard Rate Functions, 212

5.6. Other Continuous Distributions, 215

5.6.1. The Gamma Distribution, 215

5.6.2. The Weibull Distribution, 216

5.6.3. The Cauchy Distribution, 217

5.6.4. The Beta Distribution, 218

5.7. The Distribution of a Function of a Random Variable, 219

Theoretical Exercises, 227

Self-Test Problems and Exercises, 229

6. Jointly Distributed Random Variables, 232

6.1. Joint Distribution Functions, 232

6.2. Independent Random Variables, 240

6.3. Sums of Independent Random Variables, 252

6.3.1. Identically Distributed Uniform Random Variables, 252

6.3.2. Gamma Random Variables, 254

6.3.3. Normal Random Variables, 256

6.3.4. Poisson and Binomial Random Variables, 259

6.3.5. Geometric Random Variables, 260

6.4. Conditional Distributions: Discrete Case, 263

6.5. Conditional Distributions: Continuous Case, 266

6.6. Order Statistics, 270

6.7. Joint Probability Distribution of Functions of Random Variables, 274

6.8. Exchangeable Random Variables, 282

Theoretical Exercises, 291

Self-Test Problems and Exercises, 293

7. Properties of Expectation, 297

7.1. Introduction, 297

7.2. Expectation of Sums of Random Variables, 298

7.2.1. Obtaining Bounds from Expectations via the Probabilistic Method, 311

7.2.2. The Maximum–Minimums Identity, 313

7.3. Moments of the Number of Events that Occur, 315

7.4. Covariance, Variance of Sums, and Correlations, 322

7.5. Conditional Expectation, 331

7.5.1. Definitions, 331

7.5.2. Computing Expectations by Conditioning, 333

7.5.3. Computing Probabilities by Conditioning, 344

7.5.4. Conditional Variance, 347

7.6. Conditional Expectation and Prediction, 349

7.7. Moment Generating Functions, 354

7.7.1. Joint Moment Generating Functions, 363

7.8. Additional Properties of Normal Random Variables, 365

7.8.1. The Multivariate Normal Distribution, 365

7.8.2. The Joint Distribution of the Sample Mean and Sample Variance, 367

7.9. General Definition of Expectation, 369

Theoretical Exercises, 380

Self-Test Problems and Exercises, 384

8. Limit Theorems, 388

8.1. Introduction, 388

8.2. Chebyshev’s Inequality and the Weak Law of Large Numbers, 388

8.3. The Central Limit Theorem, 391

8.4. The Strong Law of Large Numbers, 400

8.5. Other Inequalities, 403

8.6. Bounding the Error Probability When Approximating a Sum of Independent Bernoulli Random Variables by a Poisson Random Variable, 410

Theoretical Exercises, 414

Self-Test Problems and Exercises, 415

9. Additional Topics in Probability, 417

9.1. The Poisson Process, 417

9.2. Markov Chains, 419

9.3. Surprise, Uncertainty, and Entropy, 425

9.4. Coding Theory and Entropy, 428

Problems and Theoretical Exercises, 435

Self-Test Problems and Exercises, 436

References, 436

10. Simulation, 438

10.1. Introduction, 438

10.2. General Techniques for Simulating Continuous Random Variables, 440

10.2.1. The Inverse Transformation Method, 441

10.2.2. The Rejection Method, 442

10.3. Simulating from Discrete Distributions, 447

10.4. Variance Reduction Techniques, 449

10.4.1. Use of Antithetic Variables, 450

10.4.2. Variance Reduction by Conditioning, 451

10.4.3. Control Variates, 452

Self-Test Problems and Exercises, 455

Answers to Selected Problems, 457

Solutions to Self-Test Problems and Exercises, 461

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