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

(11 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • L07.1 Lecture Overview (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
2
  • L07.2 Conditional PMFs (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
3
  • L07.3 Conditional Expectation & the Total Expectation Theorem (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
4
  • L07.4 Independence of Random Variables (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
5
  • L07.5 Example (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
6
  • L07.6 Independence & Expectations (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
7
  • L07.7 Independence, Variances & the Binomial Variance (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
8
  • L07.8 The Hat Problem (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
9
  • S07.1 The Inclusion-Exclusion Formula (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
10
  • S07.2 The Variance of the Geometric (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
11
  • S07.3 Independence of Random Variables Versus Independence of Events (M-I-T)
Lecture 7: Discrete Random Variables Part III (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences

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