랜덤프로세스 개론
- 건국대학교
- 이향원
페이스북
트위터
카카오톡
네이버밴드
링크복사
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- 주제분류
- 공학 >컴퓨터ㆍ통신 >컴퓨터공학
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- 강의학기
- 2013년 2학기
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- 조회수
- 29,337
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- 평점
- 5/5.0 (6)
This course covers the fundamental concepts of probability and stochastic processes including:
sample space, conditional probability, independence, random variables, limit theorems, basic stochastic processes and Markov Chains. These concepts are widely used in science and engineering in order to describe and analyze uncertain phenomena in the real world. Our goal is to get familiar with these concepts so that we can apply them in the subsequent courses such
as Digital Image Processing, (Multimedia) Signal Processing, etc.
sample space, conditional probability, independence, random variables, limit theorems, basic stochastic processes and Markov Chains. These concepts are widely used in science and engineering in order to describe and analyze uncertain phenomena in the real world. Our goal is to get familiar with these concepts so that we can apply them in the subsequent courses such
as Digital Image Processing, (Multimedia) Signal Processing, etc.
- 수강안내 및 수강신청
- ※ 수강확인증 발급을 위해서는 수강신청이 필요합니다
Introduction. Probability axioms and random variables
차시별 강의
| 1. | |
Introduction. Probability axioms and random variables | PDF/PMF and CDF |  |
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Introduction. Probability axioms and random variables | PDF/PMF and CDF | |
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| 2. | |
Function of random variables Definitions of convergence | Convergence in probability, convergence with probability 1, convergence in distribution | |
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Function of random variables Definitions of convergence | Convergence in probability, convergence with probability 1, convergence in distribution | |
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| 3. | |
Useful inequalities and law of large numbers. Central limit theorem | Markov inequality, Chebyshev inequality, Chernoff bound | |
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Useful inequalities and law of large numbers. Central limit theorem | Markov inequality, Chebyshev inequality, Chernoff bound | |
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| 4. | |
Bernoulli process and Poisson process | Definitions and properties of Bernoulli and Poisson processes | |
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Bernoulli process and Poisson process | Definitions and properties of Bernoulli and Poisson processes | |
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| 5. | |
Discrete-time Markov chains and steady-state behavior | Definition, state transition probability, Markov property | |
| 6. | |
Mixing time and midterm review | Role of second largest eigenvalues and midterm review | |
| 7. | |
M/M/1 queues | Poisson arrival and exponential service, analysis of waiting times | |
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M/M/1 queues | Poisson arrival and exponential service, analysis of waiting times | |
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| 8. | |
M/G/1 queues and Pollaczek- Khinchin formula | Definition of M/G/1 queue and derivation of Pollaczek-Khinchin formula | |
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M/G/1 queues and Pollaczek- Khinchin formula | Definition of M/G/1 queue and derivation of Pollaczek-Khinchin formula | |
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| 9. | |
Estimation theory and Expectation- Maximization (EM) algorithm | Bayesian estimation, expectation maximization | |
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Estimation theory and Expectation- Maximization (EM) algorithm | Bayesian estimation, expectation maximization | |
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| 10. | |
Hidden Markov models (HMM) | Modeling uncertain pheonomena using hidden Markov models | |
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Hidden Markov models (HMM) | Modeling uncertain pheonomena using hidden Markov models | |
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| 11. | |
Counting processes and Renewal processes | Definition of counting and renewal processes, and analysis | |
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Counting processes and Renewal processes | Definition of counting and renewal processes, and analysis | |
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| 12. | |
Randomized algorithms | Applications of probability and stochastic processes to randomized algorithms | |
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Randomized algorithms | Applications of probability and stochastic processes to randomized algorithms | |
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| 13. | |
Randomized algorithms and course review | Applications of probability and stochastic processes to randomized algorithms | |
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Randomized algorithms and course review | Applications of probability and stochastic processes to randomized algorithms | |
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