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

(8 Lectures Available)

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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