A First Course in Probabilitytxt,chm,pdf,epub,mobi下载 作者:Sheldon Ross 出版社: Pearson Prentice Hall 出版年: 2009-1-7 页数: 552 定价: USD 170.67 装帧: Hardcover ISBN: 9780136033134 内容简介 · · · · · ·A First Course in Probability, Eighth Edition , features clear and intuitive explanations of the mathematics of probability theory, outstanding problem sets, and a variety of diverse examples and applications. This book is ideal for an upper-level undergraduate or graduate level introduction to probability for math, science, engineering and business students. It assumes a backg... 作者简介 · · · · · ·Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction ... 目录 · · · · · ·ContentsPreface xi 1 Combinatorial Analysis 1 1.1 Introduction . . . .............................. 1 1.2 The Basic Principle of Counting . . . ................... 1 1.3 Permutations................................. 3 · · · · · ·() Contents Preface xi 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 Summary . .................................. 15 Problems ................................... 16 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 Summary . .................................. 49 Problems ................................... 50 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 IndependentEvents............................. 79 3.5 P (· |F ) Is a Probability . . . . . ....................... 93 Summary . .................................. 101 Problems ................................... 102 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 vii viii Contents 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 TheZeta(orZipf)Distribution.................. 163 4.9 Expected Value of Sums of Random Variables . ............ 164 4.10 Properties of the Cumulative Distribution Function . . . . . ...... 168 Summary . .................................. 170 Problems ................................... 172 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 Summary . .................................. 222 Problems ................................... 224 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 Summary . .................................. 285 Problems ................................... 287 Theoretical Exercises . . . . . ....................... 291 Self-Test Problems and Exercises . . ................... 293 Contents ix 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 Summary . .................................. 370 Problems ................................... 373 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 TheCentralLimitTheorem ........................ 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 Summary . .................................. 412 Problems ................................... 412 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 Summary . .................................. 434 Problems and Theoretical Exercises . ................... 435 Self-Test Problems and Exercises . . ................... 436 References .................................. 436 x Contents 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 Summary . .................................. 453 Problems ................................... 453 Self-Test Problems and Exercises . . ................... 455 Reference .................................. 455 Answers to Selected Problems 457 Solutions to Self-Test Problems and Exercises 461 Index · · · · · · () |
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一本书写出自己想看的内容
果然有独到的见解