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Electrical Engineering and Computer Science (M-I-T)
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Introduction to Probability (Spring 2018) (M-I-T)
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Part I: The Fundamentals (M-I-T)
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Lecture 6: Discrete Random Variables Part II (M-I-T)
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
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