Geometric Distribution, Poisson

In summary, the problem involves finding the probability of M being greater than 1, given that N has a geometric distribution with a probability of 0 and M has a Poisson distribution. The expected values of both variables are equal and the variance of N is twice that of M. To solve for this probability, we use the expected value and variance formulas for geometric and Poisson distributions and solve for lambda.
  • #1
Mad Scientists
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The problem is the following;

N has a geometric distribution with Pr(N=0)>0. M has a Poisson distribution. You are given:

E(N) = E(M); Var(N) = 2Var(M)

Calculate Pr (M>1).

From general knowledge we know that the expected value of a variable in a geometric distribution E(N) = q/p, and Var(N) = q/(p^2).
Also; the expected value of a variable in a Poisson distribution E(M) = lambda and Var(M) also = lambda.

I believe that the answer is 1 - pr(M=0) - pr(M=1) which is the equivalent of
1-e^(-lambda)-lambda*e^(-lambda).

But this would require solving for lambda, a feat I have not yet accomplished.


Any pointers?..

Thanks in advance,

Teddy
 
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  • #2
You should be able to solve for p and [itex]\lambda[/itex], from [itex]\lambda[/itex] = (1-p)/p, and 2[itex]\lambda[/itex] = (1-p)/(p^2).

Note that Pr(N=0) = p > 0 so [itex]\lambda < +\infty[/itex].
 
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  • #3
Thanks Enuma, I was able to solve for it.
 
  • #4
NM i saw the reply.
 

FAQ: Geometric Distribution, Poisson

1. What is the Geometric Distribution?

The Geometric Distribution is a probability distribution that describes the number of trials needed to achieve the first success in a series of independent and identical trials. It is often used to model events that have a constant probability of success, such as flipping a coin or rolling a dice.

2. How is the Geometric Distribution different from the Binomial Distribution?

The Binomial Distribution describes the probability of obtaining a certain number of successes in a fixed number of trials, while the Geometric Distribution focuses on the number of trials needed to achieve the first success. Additionally, the Binomial Distribution assumes a finite number of trials, while the Geometric Distribution assumes an infinite number of trials.

3. What is the Poisson Distribution?

The Poisson Distribution is a probability distribution that describes the number of events that occur in a fixed interval of time or space, given a known average rate of occurrence. It is often used to model rare events, such as the number of customers arriving at a store in a given hour or the number of accidents on a highway in a day.

4. How is the Poisson Distribution related to the Geometric Distribution?

The Poisson Distribution can be seen as a continuous version of the Geometric Distribution. While the Geometric Distribution focuses on the number of trials needed to achieve the first success, the Poisson Distribution considers the entire number of successes in a given interval of time or space.

5. What are some real-life applications of the Geometric and Poisson Distributions?

The Geometric Distribution is commonly used in fields such as finance, manufacturing, and quality control to model the success rate of processes or products. The Poisson Distribution is often used in fields such as insurance, epidemiology, and traffic engineering to model the occurrence of rare events.

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