MIT

L22.6 A Simple Example (M-I-T)

L23.1 Lecture Overview (M-I-T)

L22.7 Time of the K–th Arrival (M-I-T)

L23.2 The Sum of Independent Poisson Random Variables (M-I-T)

L23.3 Merging Independent Poisson Processes (M-I-T)

L22.8 The Fresh Start Property and Its Implications (M-I-T)

L22.9 Summary of Results (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)

L24.1 Lecture Overview (M-I-T)

L23.6 Splitting a Poisson Process (M-I-T)

L24.2 Introduction to Markov Processes (M-I-T)

L23.7 Random Incidence in the Poisson Process (M-I-T)

L24.3 Checkout Counter Example (M-I-T)

L23.8 Random Incidence in a Non–Poisson Process (M-I-T)

L25.10 Birth-Death Processes — Part I (M-I-T)

L24.4 Discrete-Time Finite-State Markov Chains (M-I-T)

L23.9 Different Sampling Methods Can Give Different Results (M-I-T)

L25.11 Birth-Death Processes — Part II (M-I-T)

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

L24.5 N–Step Transition Probabilities (M-I-T)

L25.1 Brief Introduction (M-I-T)

S23.2 Poisson Arrivals During an Exponential Interval (M-I-T)

L24.6 A Numerical Example — Part I (M-I-T)

L25.2 Lecture Overview (M-I-T)