Learn · Statistics for Applications

Lecture 16 of 22Lecture Videos1 h 4 min

Lecture 18: Bayesian Statistics (cont.)

Prof. Philippe Rigollet · Massachusetts Institute of Technology

At MIT OpenCourseWare Transcript

Credits and licence

From
Statistics for Applications, Massachusetts Institute of Technology (at MIT OpenCourseWare)
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
Attribution
Prof. Philippe Rigollet. Statistics for Applications. Fall 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
Who can watch
Tweens profiles and older. How courses are placed

All the lectures

  1. Lecture 1: Introduction to Statistics 1 h 19 min · Video
  2. Lecture 2: Introduction to Statistics (cont.) 1 h 18 min · Video
  3. Lecture 3: Parametric Inference 1 h 23 min · Video
  4. Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation 1 h 18 min · Video
  5. Lecture 5: Maximum Likelihood Estimation (cont.) 1 h 17 min · Video
  6. Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments 1 h 20 min · Video
  7. Lecture 7: Parametric Hypothesis Testing 1 h 19 min · Video
  8. Lecture 8: Parametric Hypothesis Testing (cont.) 1 h 19 min · Video
  9. Lecture 9: Parametric Hypothesis Testing (cont.) 1 h 22 min · Video
  10. Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit 1 h 23 min · Video
  11. Lecture 12: Testing Goodness of Fit (cont.) 1 h 22 min · Video
  12. Lecture 13: Regression 1 h 17 min · Video
  13. Lecture 14: Regression (cont.) 1 h 14 min · Video
  14. Lecture 15: Regression (cont.) 1 h 16 min · Video
  15. Lecture 17: Bayesian Statistics 1 h 19 min · Video
  16. Lecture 18: Bayesian Statistics (cont.) 1 h 4 min · Video
  17. Lecture 19: Principal Component Analysis 1 h 18 min · Video
  18. Lecture 20: Principal Component Analysis (cont.) 1 h 17 min · Video
  19. Lecture 21: Generalized Linear Models 1 h 16 min · Video
  20. Lecture 22: Generalized Linear Models (cont.) 1 h 18 min · Video
  21. Lecture 23: Generalized Linear Models (cont.) 1 h 19 min · Video
  22. Lecture 24: Generalized Linear Models (cont.) 55 min · Video