Lecture Note @ KAIST 2018 (Syllabus)

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