John Tsitsiklis: Probabilistic Systems Analysis and Applied Probability (Fall 2010, MIT)

# automatic playing for the 25 videos (click the up-left corner for the list)

source: MIT OpenCourseWare      Last updated on 2014年7月2日
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010
View the complete course: http://ocw.mit.edu/6-041F10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

1. Probability Models and Axioms 51:11
2. Conditioning and Bayes' Rule 51:11
3. Independence 46:30
4. Counting 51:35
5. Discrete Random Variables I 50:35
6. Discrete Random Variables II 50:53
7. Discrete Random Variables III 50:42
8. Continuous Random Variables 50:29
9. Multiple Continuous Random Variables 50:51
10. Continuous Bayes' Rule; Derived Distributions 48:53
11. Derived Distributions (ctd.); Covariance 51:55
12. Iterated Expectations 47:54
13. Bernoulli Process 50:58
14. Poisson Process I 52:44
15. Poisson Process II 49:28
16. Markov Chains I
17. Markov Chains II 51:25
18. Markov Chains III 51:50
19. Weak Law of Large Numbers 50:13
20. Central Limit Theorem 51:23
21. Bayesian Statistical Inference I 48:50
22. Bayesian Statistical Inference II 52:16
23. Classical Statistical Inference I 49:32
24. Classical Inference II 51:50
25. Classical Inference III 52:07