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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Probabilistic Systems Analysis and Applied Probability (Fall 2010) (M-I-T) » Lecture 6: Discrete Random Variable Examples; Joint PMFs (M-I-T)

Lecture 6: Discrete Random Variable Examples; Joint PMFs (M-I-T)

Course:Probabilistic Systems Analysis and Applied Probability (Fall 2010) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. John Tsitsiklis
Level:Undergraduate

Probabilistic Systems Analysis and Applied Probability (Fall 2010) (M-I-T)

Lecture 10: Continuous Bayes' Rule; Derived Distributions (M-I-T)
Lecture 11: Derived Distributions; Convolution; Covariance and Correlation (M-I-T)
Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables (M-I-T)
Lecture 13: Bernoulli Process (M-I-T)
Lecture 14: Poisson Process I (M-I-T)
Lecture 15: Poisson Process II (M-I-T)
Lecture 16: Markov Chains I (M-I-T)
Lecture 17: Markov Chains II (M-I-T)
Lecture 18: Markov Chains III (M-I-T)
Lecture 19: Weak Law of Large Numbers (M-I-T)
Lecture 1: Probability Models and Axioms (M-I-T)
Lecture 20: Central Limit Theorem (M-I-T)
Lecture 21: Bayesian Statistical Inference I (M-I-T)
Lecture 22: Bayesian Statistical Inference II (M-I-T)
Lecture 23: Classical Statistical Inference I (M-I-T)
Lecture 24: Classical Inference II (M-I-T)
Lecture 25: Classical Inference III; Course Overview (M-I-T)
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
Lecture 3: Independence (M-I-T)
Lecture 4: Counting (M-I-T)

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