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

Lecture 6 Discrete Random Variable Examples; Joint PMFs

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

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

Lecture 1 Probability Models and Axioms
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 2 Conditioning and Bayes’ Rule
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
Lecture 3 Independence
Lecture 4 Counting

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