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