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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.

Course Information

Source	MIT 開放式課程
科系	Mathematics
Language	English
影片數	22

課程影片 (22)

1. Introduction to Statistics

2. Introduction to Statistics (cont.)

3. Parametric Inference

4. Parametric Inference (cont.) and Maximum Likelihood Estimation

5. Maximum Likelihood Estimation (cont.)

6. Maximum Likelihood Estimation (cont.) and the Method of Moments

7. Parametric Hypothesis Testing

8. Parametric Hypothesis Testing (cont.)

9. Parametric Hypothesis Testing (cont.)

11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit

12. Testing Goodness of Fit (cont.)

13. Regression

14. Regression (cont.)

15. Regression (cont.)

17. Bayesian Statistics

18. Bayesian Statistics (cont.)

19. Principal Component Analysis

20. Principal Component Analysis (cont.)

21. Generalized Linear Models

22. Generalized Linear Models (cont.)

23. Generalized Linear Models (cont.)

24. Generalized Linear Models (cont.)

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8

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32.3%

140.05

82.02%

62,201