Probabilistic Systems Analysis and Applied Probability
Offered By: Massachusetts Institute of Technology via MIT OpenCourseWare
Course Description
Overview
Syllabus
1. Probability Models and Axioms.
2. Conditioning and Bayes' Rule.
3. Independence.
4. Counting.
5. Discrete Random Variables I.
6. Discrete Random Variables II.
7. Discrete Random Variables III.
8. Continuous Random Variables.
9. Multiple Continuous Random Variables.
10. Continuous Bayes' Rule; Derived Distributions.
11. Derived Distributions (ctd.); Covariance.
12. Iterated Expectations.
13. Bernoulli Process.
14. Poisson Process I.
15. Poisson Process II.
16. Markov Chains I.
17. Markov Chains II.
18. Markov Chains III.
19. Weak Law of Large Numbers.
20. Central Limit Theorem.
21. Bayesian Statistical Inference I.
22. Bayesian Statistical Inference II.
23. Classical Statistical Inference I.
24. Classical Inference II.
25. Classical Inference III.
Taught by
Prof. John Tsitsiklis
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