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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Introduction to Probability (Spring 2018) (M-I-T) » Part III: Random Processes (M-I-T) » Lecture 23: The Poisson Process Part II (M-I-T) » S23.1 Poisson Versus Normal Approximations to the Binomial (M-I-T)

S23.1 Poisson Versus Normal Approximations to the Binomial (M-I-T)

Course:Lecture 23: The Poisson Process Part II (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. John Tsitsiklis, Prof. Patrick Jaillet
Level:Graduate

Lecture 23: The Poisson Process Part II (M-I-T)

L23.1 Lecture Overview (M-I-T)
L23.2 The Sum of Independent Poisson Random Variables (M-I-T)
L23.3 Merging Independent Poisson Processes (M-I-T)
L23.4 Where is an Arrival of the Merged Process Coming From? (M-I-T)
L23.5 The Time Until the First (or Last) Lightbulb Burns Out (M-I-T)
L23.6 Splitting a Poisson Process (M-I-T)
L23.7 Random Incidence in the Poisson Process (M-I-T)
L23.8 Random Incidence in a Non–Poisson Process (M-I-T)
L23.9 Different Sampling Methods Can Give Different Results (M-I-T)
S23.1 Poisson Versus Normal Approximations to the Binomial (M-I-T)
S23.2 Poisson Arrivals During an Exponential Interval (M-I-T)

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