Preface |
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xi | |
1 Review of Probability |
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1 | (40) |
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1 | (1) |
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Review of Basic Probability Definitions |
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2 | (2) |
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Some Common Probability Distributions |
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4 | (4) |
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5 | (1) |
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5 | (1) |
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6 | (1) |
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Negative Binomial Distribution |
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7 | (1) |
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8 | (1) |
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Properties of a Probability Distribution |
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8 | (9) |
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9 | (2) |
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11 | (2) |
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13 | (1) |
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Expected Value of a Random Variable |
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14 | (3) |
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Properties of the Expected Value |
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17 | (1) |
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Expected Value of a Random Variable with Common Distributions |
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17 | (12) |
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17 | (1) |
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18 | (1) |
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18 | (1) |
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Negative Binomial Distribution |
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19 | (1) |
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20 | (1) |
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Functions of a Random Variable |
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21 | (3) |
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24 | (5) |
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29 | (3) |
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Probability Generating Functions |
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29 | (3) |
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Moment Generating Functions |
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32 | (1) |
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33 | (8) |
2 Discrete-Time, Finite-State Markov Chains |
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41 | (100) |
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41 | (1) |
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41 | (1) |
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42 | (9) |
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Directed Graphs: Examples of Markov Chains |
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51 | (1) |
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Random Walk with Reflecting Boundaries |
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52 | (1) |
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53 | (1) |
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54 | (1) |
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Central Problem of Markov Chains |
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55 | (1) |
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Condition to Ensure a Unique Equilibrium State |
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56 | (8) |
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Finding the Equilibrium State |
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64 | (2) |
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Transient and Recurrent States |
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66 | (3) |
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69 | (5) |
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74 | (6) |
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80 | (11) |
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91 | (2) |
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Mean Recurrence Time and the Equilibrium State |
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93 | (8) |
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Fundamental Matrix for Regular Markov Chains |
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101 | (10) |
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Dividing a Markov Chain into Equivalence Classes |
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111 | (4) |
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115 | (11) |
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126 | (3) |
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129 | (3) |
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132 | (9) |
3 Discrete-Time, Infinite-State Markov Chains |
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141 | (54) |
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141 | (14) |
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Delayed Renewal Processes |
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155 | (1) |
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Equilibrium State for Countable Markov Chains |
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156 | (1) |
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Physical Interpretation of the Equilibrium State |
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157 | (1) |
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Null Recurrent versus Positive Recurrent States |
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157 | (10) |
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167 | (7) |
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174 | (8) |
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182 | (8) |
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190 | (5) |
4 Exponential Distribution and Poisson Process |
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195 | (30) |
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Continuous Random Variables |
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195 | (1) |
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Cumulative Distribution Function (Continuous Case) |
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196 | (2) |
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198 | (2) |
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200 | (1) |
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Exponential Distribution as a Model for Arrivals |
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200 | (5) |
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Memoryless Random Variables |
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205 | (9) |
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214 | (5) |
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Poisson Processes with Occurrences of Two Types |
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219 | (3) |
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222 | (3) |
5 Continuous-Time Markov Chains |
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225 | (72) |
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225 | (3) |
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Generators of Continuous Markov Chains: The Kolmogorov Forward and Backward Equations |
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228 | (17) |
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Connections of the Infinitesimal Generator, the Embedded |
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Markov Chain, Transition Rates, and the Stationary Distribution |
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238 | (7) |
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Connection between the Steady State of a Continuous Markov Chain and the Steady State of the Embedded Matrix |
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245 | (3) |
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248 | (4) |
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Birth and Birth-Death Processes |
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252 | (4) |
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Birth Process (Forward Equation) |
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253 | (2) |
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Birth Process (Backward Equation) |
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255 | (1) |
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Birth and Death Processes |
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256 | (14) |
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Forward Equations for Birth-Death Processes |
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257 | (2) |
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Birth-Death Processes: Backward Equations |
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259 | (1) |
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Recurrence and Transience in Birth-Death Processes |
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260 | (10) |
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270 | (6) |
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271 | (2) |
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M/M/1/K Queue: One Server (Size of the Queue Is Limited) |
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273 | (3) |
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Detailed Balance Equations |
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276 | (14) |
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279 | (6) |
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M/M/c/K Queue: c Servers (Size of the Queue Is Limited to K) |
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285 | (4) |
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289 | (1) |
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290 | (7) |
6 Reversible Markov Chains |
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297 | (24) |
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Random Walks on Weighted Graphs |
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312 | (2) |
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Discrete Time Birth-Death Process as a Reversible Markov Chain |
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314 | (1) |
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Continuous-Time Reversible Markov Chains |
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315 | (3) |
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318 | (3) |
Bibliography |
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321 | (2) |
Index |
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323 | |