| 1. | |
확률 이론1 | Random Event Conditional Probability |  |
| 2. | |
확률 이론1 | Total Probability Bayes' Theorem and Independence | |
| 3. | |
확률 이론1 | Random Variable, Random Vector Characteristic and Generating Function | |
| 4. | |
랜덤 프로세스1 | Random Process Markov Processes Birth-Death processes | |
| 5. | |
랜덤 프로세스2 | Random Walks Wide Sense Stationary (WSS) Gaussian Process | |
| 6. | |
마코프 체인 1 | Discrete-time Markov Chains Homogeneous Markov Chain The Chapman-Kolmogorov equation Ergodicity | |
| 7. | |
마코프 체인 2 | Continuous-time Markov Chains Birth-Death Processes | |
| 8. | |
Poisson Processes | Poisson Processes Memoryless property Erlang distribution | |
| 9. | |
기초 대기이론 1 | Specification of queueing systems Classification of queueing systems Little’s formulas | |
| 10. | |
기초 대기이론 2 | General equilibrium solution M / M / 1 queueing system | |
| 11. | |
기초 대기이론 3 | M / M / ∞ or M / M QS : infinite number of servers M / M / m QS : the m - server case M / M / 1 / K QS : the finite storage case. | |
| 12. | |
기초 대기이론 4 | M / M / m / m QS : the m servers loss case M / M / 1 // M (M / M 1 / ∞ / M) : finite customer population - single server M / M / ∞ // M (M / M /// M) M / M / m / K / M | |
| 13. | |
기초 대기이론 5 | A remark on the applicability of steady-state solutions The equilibrium equations for Markov queues The method of stages – Erlang distribution | |
| 14. | |
기초 대기이론 6 | The M / Er / 1 QS Some further extensions of the M/Er/1 system | |
| 15. | |
M/G/1 | Pollazchek-Khinchin mean values formulas | |
|
Networks of queues | Burke’s theorem Jackson theorem | |
대구광역시 동구 동내로 64(동내동 1119) 우)41061
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