Learn · Probabilistic Systems Analysis and Applied Probability
Lecture 11 of 25Video Lectures52 min
Lecture 11: Derived Distributions; Convolution; Covariance and Correlation
Prof. John Tsitsiklis · Massachusetts Institute of Technology
Credits and licence
- From
- Probabilistic Systems Analysis and Applied Probability, 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. John Tsitsiklis. Probabilistic Systems Analysis and Applied Probability. Fall 2010. 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
- Lecture 1: Probability Models and Axioms
- Lecture 2: Conditioning and Bayes' Rule
- Lecture 3: Independence
- Lecture 4: Counting
- Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations
- Lecture 6: Discrete Random Variable Examples; Joint PMFs
- Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence
- Lecture 8: Continuous Random Variables
- Lecture 9: Multiple Continuous Random Variables
- Lecture 10: Continuous Bayes' Rule; Derived Distributions
- Lecture 11: Derived Distributions; Convolution; Covariance and Correlation
- Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables
- Lecture 13: Bernoulli Process
- Lecture 14: Poisson Process I
- Lecture 15: Poisson Process II
- Lecture 16: Markov Chains I
- Lecture 17: Markov Chains II
- Lecture 18: Markov Chains III
- Lecture 19: Weak Law of Large Numbers
- Lecture 20: Central Limit Theorem
- Lecture 21: Bayesian Statistical Inference I
- Lecture 22: Bayesian Statistical Inference II
- Lecture 23: Classical Statistical Inference I
- Lecture 24: Classical Inference II
- Lecture 25: Classical Inference III; Course Overview