3) Extra Credit Challenge: We have seen in class that the Poisson distribution with parameter A = np is a good approximation to the probability distribution of the number of successes in n independent trials when each trial has probability p of being a success, provided that n is large and p small. In fact, the Poisson distribution remains a good approximation even when the trials are not independent, provided that their dependence is weak. Furthermore, it is not required that each trial be identical. For example, if the i-th trial had a probability p; of being a success (i =1, 2,...,n), then the total number of successes that occur in the n trials is well approximated by a Poisson random variable with A = n E, Pi, if each Pi is "small" and each trial is independent or only "weakly dependent." This is called the Poisson paradigm, . Suppose an experiment has n identical and independent trials, with each trial having k possible outcomes with probabilities p1,..., Pk respectively. Using the Poisson paradigm, show that if all the p;'s are small then the probability that no trial outcome occurs more than once is approximately equal to exp(-n(n – 1) E=, P /2).
3) Extra Credit Challenge: We have seen in class that the Poisson distribution with parameter A = np is a good approximation to the probability distribution of the number of successes in n independent trials when each trial has probability p of being a success, provided that n is large and p small. In fact, the Poisson distribution remains a good approximation even when the trials are not independent, provided that their dependence is weak. Furthermore, it is not required that each trial be identical. For example, if the i-th trial had a probability p; of being a success (i =1, 2,...,n), then the total number of successes that occur in the n trials is well approximated by a Poisson random variable with A = n E, Pi, if each Pi is "small" and each trial is independent or only "weakly dependent." This is called the Poisson paradigm, . Suppose an experiment has n identical and independent trials, with each trial having k possible outcomes with probabilities p1,..., Pk respectively. Using the Poisson paradigm, show that if all the p;'s are small then the probability that no trial outcome occurs more than once is approximately equal to exp(-n(n – 1) E=, P /2).
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 3 steps
Similar questions
Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman