| 1. | |
강좌 개요 및 확률 모델 | Course objective and outline, models and probabilistic models, typical examples of probabilistic models |  |
| 2. | |
확률 이론의 기본 개념 I | Axioms of probability, discrete and continuous sample spaces, counting methods, conditional probability | |
| 3. | |
확률 이론의 기본 개념 II | Independence of events, sequence of independent experiments, sequence of dependent experiments, random number generators | |
| 4. | |
이산확률변수 I | Definition of random variable, probability mass function (pmf), expected value of a random variable, variance of a random variable | |
| 5. | |
이산확률변수 II | Conditional pmf, conditional expected value, important discrete random variables, generation of discrete random variables | |
| 6. | |
단일 확률변수 I | Cumulative distribution function (cdf), types of random variables, probability density function (pdf), conditional cdf's and pdf's, expected value of function of a random variable | |
| 7. | |
단일 확률변수 II | Variance and other parameters, important continuous random variables, function of a random variable | |
| 8. | |
단일 확률변수 III | Useful inequalities, transform methods, generation of random variables | |
| 9. | |
다중 확률변수 I | Two random variables, joint and marginal pmf, joint and marginal cdf, joint and marginal pdf | |
| 10. | |
다중 확률변수 II | Independence of multiple random variables, joint moment, correlation and covariance, conditional probability | |
| 11. | |
다중 확률변수 III | Conditional expectation, functions of multiple random variables, jointly Gaussian random variables | |
| 12. | |
확률변수의 합 | Sums of random variables, pdf of sums of random variables, sample mean, law of large number, central limit theorem | |
| 13. | |
통계 입문 | samples and sampling distributions, parameter estimation, properties of estimators, hypothesis testing | |
| 14. | |
확률과정 입문 | Definition and interpretation of random process, categorization of random processes, specification of random process, stationary random process | |
대구광역시 동구 동내로 64(동내동 1119) 우)41061
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