Lecture 6: Discrete Random Variables Part II (M-I-T)

S#	Lecture	Course	Institute	Instructor	Discipline
1	L06.1 Lecture Overview (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
2	L06.2 Variance (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
3	L06.3 The Variance of the Bernoulli & the Uniform (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
4	L06.4 Conditional PMFs & Expectations Given an Event (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
5	L06.5 Total Expectation Theorem (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
6	L06.6 Geometric PMF Memorylessness & Expectation (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
7	L06.7 Joint PMFs and the Expected Value Rule (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
8	L06.8 Linearity of Expectations & the Mean of the Binomial (M-I-T)	Lecture 6: Discrete Random Variables Part II (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences

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