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Lecture 11: Derived Distributions (M-I-T)

S#	Lecture	Course	Institute	Instructor	Discipline
1	L11.1 Lecture Overview (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
2	L11.2 The PMF of a Function of a Discrete Random Variable (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
3	L11.3 A Linear Function of a Continuous Random Variable (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
4	L11.4 A Linear Function of a Normal Random Variable (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
5	L11.5 The PDF of a General Function (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
6	L11.6 The Monotonic Case (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
7	L11.7 The Intuition for the Monotonic Case (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
8	L11.8 A Nonmonotonic Example (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
9	L11.9 The PDF of a Function of Multiple Random Variables (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences
10	S11.1 Simulation (M-I-T)	Lecture 11: Derived Distributions (M-I-T)	MIT	Prof. John Tsitsiklis, Prof. Patrick Jaillet	Applied Sciences