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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Introduction to Probability (Spring 2018) (M-I-T) » Part I: The Fundamentals (M-I-T) » Lecture 7: Discrete Random Variables Part III (M-I-T) » L07.1 Lecture Overview (M-I-T)

L07.1 Lecture Overview (M-I-T)

Course:Lecture 7: Discrete Random Variables Part III (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. John Tsitsiklis, Prof. Patrick Jaillet
Level:Graduate

Lecture 7: Discrete Random Variables Part III (M-I-T)

L07.1 Lecture Overview (M-I-T)
L07.2 Conditional PMFs (M-I-T)
L07.3 Conditional Expectation & the Total Expectation Theorem (M-I-T)
L07.4 Independence of Random Variables (M-I-T)
L07.5 Example (M-I-T)
L07.6 Independence & Expectations (M-I-T)
L07.7 Independence, Variances & the Binomial Variance (M-I-T)
L07.8 The Hat Problem (M-I-T)
S07.1 The Inclusion-Exclusion Formula (M-I-T)
S07.2 The Variance of the Geometric (M-I-T)
S07.3 Independence of Random Variables Versus Independence of Events (M-I-T)

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