Statistics for Applications (Fall 2016)

Statistics for Applications (Fall 2016)

Prof. Philippe Rigollet via MIT OpenCourseWare Direct link

19. Principal Component Analysis

17 of 22

17 of 22

19. Principal Component Analysis

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Classroom Contents

Statistics for Applications (Fall 2016)

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  1. 1 1. Introduction to Statistics
  2. 2 2. Introduction to Statistics (cont.)
  3. 3 3. Parametric Inference
  4. 4 4. Parametric Inference (cont.) and Maximum Likelihood Estimation
  5. 5 5. Maximum Likelihood Estimation (cont.)
  6. 6 6. Maximum Likelihood Estimation (cont.) and the Method of Moments
  7. 7 7. Parametric Hypothesis Testing
  8. 8 8. Parametric Hypothesis Testing (cont.)
  9. 9 9. Parametric Hypothesis Testing (cont.)
  10. 10 11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
  11. 11 12. Testing Goodness of Fit (cont.)
  12. 12 13. Regression
  13. 13 14. Regression (cont.)
  14. 14 15. Regression (cont.)
  15. 15 17. Bayesian Statistics
  16. 16 18. Bayesian Statistics (cont.)
  17. 17 19. Principal Component Analysis
  18. 18 20. Principal Component Analysis (cont.)
  19. 19 21. Generalized Linear Models
  20. 20 22. Generalized Linear Models (cont.)
  21. 21 23. Generalized Linear Models (cont.)
  22. 22 24. Generalized Linear Models (cont.)

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