Lecture Note @ KAIST 2018 (Syllabus)
- Introduction (Slide) - 8/27 강의
- Probability (Slide) - 8/29 강의
- Random variable (Slide) - 9/3 강의
- Expectation etc (Slide) - 9/5 강의
- Bivariate distribution (Slide) - 9/10 강의
- Conditional distributions (Slide) - 9/12 강의
- Extension to several random variables (Slide) - 9/17 강의
- Normal Distribution (Slide) - 9/19 강의
- Gamma, Chi-square, and best distribution (Slide) - 9/26 강의
- T-distribution and F-distribution (Slide) - 10/1 강의
- Binomial and Poisson distribution (Slide) - 10/8 강의
- Some elementary statistical Inference (Slide) - 10/10 강의
- Introduction to hypothesis testing (Slide) - 10/22 강의
- Convergence in probability (Slide) - 10/24 강의
- Convergence in distribution, CLT (Slide) - 10/29 강의
- Maximum likelihood estimation (Slide) - 11/5 강의
- Rao-Cramer lower bound and efficiency (Slide) - 11/7 강의
- Likelihood Ratio test (Slide) - 11/12 강의
- Goodness of fit test (Slide) - 11/14 강의
- Sufficiency 1 (Slide) - 11/19 강의
- Sufficiency 2 (Slide) - 11/26 강의
- Most Powerful Tests (Slide) - 12/5 강의
- Likelihood ratio test (Slide) - 1/21 강의 (Video1; Video2)
- ANOVA (Slide) - 1/30 강의