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Uedu Open / Statistics for Applications
18.650

Statistics for Applications

Prof. Philippe Rigollet | Fall 2016
Science & Math Mathematics Probability and Statistics
前往原始課程
CC BY-NC-SA 4.0
課程簡介
This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.
課程資訊
來源MIT 開放式課程
科系Mathematics
語言English
影片數22
課程影片 (22)
1
1. Introduction to Statistics
1. Introduction to Statistics
2
2. Introduction to Statistics (cont.)
2. Introduction to Statistics (cont.)
3
3. Parametric Inference
3. Parametric Inference
4
4. Parametric Inference (cont.) and Maximum Likelihood Estimation
4. Parametric Inference (cont.) and Maximum Likelihood Estimation
5
5. Maximum Likelihood Estimation (cont.)
5. Maximum Likelihood Estimation (cont.)
6
6. Maximum Likelihood Estimation (cont.) and the Method of Moments
6. Maximum Likelihood Estimation (cont.) and the Method of Moments
7
7. Parametric Hypothesis Testing
7. Parametric Hypothesis Testing
8
8. Parametric Hypothesis Testing (cont.)
8. Parametric Hypothesis Testing (cont.)
9
9. Parametric Hypothesis Testing (cont.)
9. Parametric Hypothesis Testing (cont.)
10
11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
11
12. Testing Goodness of Fit (cont.)
12. Testing Goodness of Fit (cont.)
12
13. Regression
13. Regression
13
14. Regression (cont.)
14. Regression (cont.)
14
15. Regression (cont.)
15. Regression (cont.)
15
17. Bayesian Statistics
17. Bayesian Statistics
16
18. Bayesian Statistics (cont.)
18. Bayesian Statistics (cont.)
17
19. Principal Component Analysis
19. Principal Component Analysis
18
20. Principal Component Analysis (cont.)
20. Principal Component Analysis (cont.)
19
21. Generalized Linear Models
21. Generalized Linear Models
20
22. Generalized Linear Models (cont.)
22. Generalized Linear Models (cont.)
21
23. Generalized Linear Models (cont.)
23. Generalized Linear Models (cont.)
22
24. Generalized Linear Models (cont.)
24. Generalized Linear Models (cont.)