Preface |
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ix | |
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1 Foundations of Probability Theory |
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1 | (41) |
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1.1 Probabilistic Foundations |
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3 | (4) |
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1.2 Classical Probability Model |
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7 | (8) |
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1.3 Geometric Probability Model |
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15 | (4) |
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1.4 Compound Chance Experiments |
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19 | (6) |
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25 | (11) |
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1.6 Inclusion-Exclusion Rule |
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36 | (6) |
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2 Conditional Probability |
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42 | (43) |
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2.1 Concept of Conditional Probability |
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42 | (5) |
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2.2 Chain Rule for Conditional Probabilities |
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47 | (7) |
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2.3 Law of Conditional Probability |
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54 | (13) |
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2.4 Bayes' Rule in Odds Form |
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67 | (10) |
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2.5 Bayesian Inference - Discrete Case |
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77 | (8) |
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3 Discrete Random Variables |
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85 | (61) |
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3.1 Concept of a Random Variable |
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85 | (4) |
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89 | (10) |
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3.3 Expected Value of Sums of Random Variables |
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99 | (7) |
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3.4 Substitution Rule and Variance |
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106 | (7) |
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3.5 Independence of Random Variables |
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113 | (5) |
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3.6 Binomial Distribution |
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118 | (6) |
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124 | (11) |
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3.8 Hypergeometric Distribution |
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135 | (5) |
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3.9 Other Discrete Distributions |
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140 | (6) |
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4 Continuous Random Variables |
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146 | (63) |
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4.1 Concept of Probability Density |
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147 | (9) |
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4.2 Expected Value of a Continuous Random Variable |
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156 | (4) |
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4.3 Substitution Rule and the Variance |
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160 | (4) |
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4.4 Uniform and Triangular Distributions |
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164 | (3) |
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4.5 Exponential Distribution |
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167 | (10) |
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4.6 Gamma, Weibull, and Beta Distributions |
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177 | (3) |
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180 | (13) |
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4.8 Other Continuous Distributions |
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193 | (5) |
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4.9 Inverse-Transformation Method and Simulation |
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198 | (4) |
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4.10 Failure-Rate Function |
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202 | (3) |
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4.11 Probability Distributions and Entropy |
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205 | (4) |
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5 Jointly Distributed Random Variables |
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209 | (30) |
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5.1 Joint Probability Mass Function |
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209 | (3) |
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5.2 Joint Probability Density Function |
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212 | (7) |
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5.3 Marginal Probability Densities |
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219 | (9) |
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5.4 Transformation of Random Variables |
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228 | (5) |
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5.5 Covariance and Correlation Coefficient |
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233 | (6) |
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6 Multivariate Normal Distribution |
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239 | (22) |
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6.1 Bivariate Normal Distribution |
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239 | (9) |
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6.2 Multivariate Normal Distribution |
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248 | (2) |
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6.3 Multidimensional Central Limit Theorem |
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250 | (7) |
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257 | (4) |
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7 Conditioning by Random Variables |
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261 | (41) |
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7.1 Conditional Distributions |
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262 | (7) |
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7.2 Law of Conditional Probability for Random Variables |
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269 | (7) |
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7.3 Law of Conditional Expectation |
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276 | (7) |
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7.4 Conditional Expectation as a Computational Tool |
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283 | (11) |
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7.5 Bayesian Inference - Continuous Case |
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294 | (8) |
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302 | (19) |
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302 | (9) |
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8.2 Branching Processes and Generating Functions |
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311 | (2) |
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8.3 Moment-Generating Functions |
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313 | (5) |
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8.4 Central Limit Theorem Revisited |
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318 | (3) |
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9 Additional Topics in Probability |
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321 | (27) |
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9.1 Bounds and Inequalities |
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321 | (6) |
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9.2 Strong Law of Large Numbers |
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327 | (8) |
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335 | (4) |
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9.4 Renewal-Reward Processes |
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339 | (9) |
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10 Discrete-Time Markov Chains |
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348 | (55) |
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349 | (8) |
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10.2 Time-Dependent Analysis of Markov Chains |
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357 | (5) |
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10.3 Absorbing Markov Chains |
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362 | (11) |
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10.4 Long-Run Analysis of Markov Chains |
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373 | (13) |
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10.5 Markov Chain Monte Carlo Simulation |
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386 | (17) |
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11 Continuous-Time Markov Chains |
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403 | (35) |
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403 | (11) |
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11.2 Time-Dependent Probabilities |
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414 | (6) |
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11.3 Limiting Probabilities |
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420 | (18) |
Appendix A Counting Methods |
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438 | (5) |
Appendix B Basics of Set Theory |
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443 | (4) |
Appendix C Some Basic Results from Calculus |
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447 | (4) |
Appendix D Basics of Monte Carlo Simulation |
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451 | (12) |
Answers to Odd-Numbered Problems |
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463 | (69) |
Index |
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532 | |