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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 5: Discrete Random Variables Part I (M-I-T) » L05.4 Bernoulli & Indicator Random Variables (M-I-T)

L05.4 Bernoulli & Indicator Random Variables (M-I-T)

Course:Lecture 5: Discrete Random Variables Part I (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. John Tsitsiklis, Prof. Patrick Jaillet
Level:Graduate

Lecture 5: Discrete Random Variables Part I (M-I-T)

L05.10 The Expected Value Rule (M-I-T)
L05.11 Linearity of Expectations (M-I-T)
L05.1 Lecture Overview (M-I-T)
L05.2 Definition of Random Variables (M-I-T)
L05.3 Probability Mass Functions (M-I-T)
L05.4 Bernoulli & Indicator Random Variables (M-I-T)
L05.5 Uniform Random Variables (M-I-T)
L05.6 Binomial Random Variables (M-I-T)
L05.7 Geometric Random Variables (M-I-T)
L05.8 Expectation (M-I-T)
L05.9 Elementary Properties of Expectation (M-I-T)
S05.1 Supplement: Functions (M-I-T)

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