Stats/Probability: Joint Exponential Distribution

In summary: OK, never mind about the Poisson stuff. In summary, the probability that three of the components fail between times 1 and 2 is 0.0925, and the probability that four of the components fail between times 2 and 3 is also 0.0925.
  • #1
mrxjbud
2
0

Homework Statement



Suppose that X=time to failure for a component has an exponential distribution with lambda =.25. Suppose that 9 of the components are selected and their failure times noted. Compute the probability that 3 of the components fail between times 1 and 2, and 4 of the components fail between times 2 and 3. Assume that the failure times are independent.

Homework Equations



Exponential Distribution: f(x;λ)=λe-xλ

The Attempt at a Solution



F(x,y)=∫∫0.25(e^(-x/4)*e^(-y/4))dxdy

I solved these over the intervals 2 to 1 and 3 to 2, and came up with F(1<x<2,2<y<3)=0.0925 as a solution for the probability that a component will fail. However, I'm not sure how to apply this to the 3/9 and 4/9 attempts. Am I even on the right track here?
 
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  • #2
mrxjbud said:

Homework Statement



Suppose that X=time to failure for a component has an exponential distribution with lambda =.25. Suppose that 9 of the components are selected and their failure times noted. Compute the probability that 3 of the components fail between times 1 and 2, and 4 of the components fail between times 2 and 3. Assume that the failure times are independent.

Homework Equations



Exponential Distribution: f(x;λ)=λe-xλ

The Attempt at a Solution



F(x,y)=∫∫0.25(e^(-x/4)*e^(-y/4))dxdy

I solved these over the intervals 2 to 1 and 3 to 2, and came up with F(1<x<2,2<y<3)=0.0925 as a solution for the probability that a component will fail. However, I'm not sure how to apply this to the 3/9 and 4/9 attempts. Am I even on the right track here?

The question's wording is a bit ambiguous. I can't figure out if you are being asked for (i) the probability that 3 fail between times 1 and 2, and (ii)[separately] the probability that 4 fail between times 2 and 3, or whether you are being asked for the joint probability that 3 fail between 1 and 2 and 4 fail between 2 and 3 (all considered as a single event).

Anyway, it is for such questions that the Poisson distribution was invented. Google Poisson process and Poisson distribution. The "memoryless" property of the exponential distribution is also very important in such cases.

RGV
 
  • #3
It's meant to be as a joint probability. I'm a little confused by your response though, I thought Poisson was mostly used for when you're only given a mean/average success rate and need to solve using it? I'm assuming here you mean to find this rate and then apply it?

In the case of an exponential, μ=1/λ so the average time to fail should be 4, correct? So then I would plug that into a Poisson formula to get a solution?
 
  • #4
mrxjbud said:
It's meant to be as a joint probability. I'm a little confused by your response though, I thought Poisson was mostly used for when you're only given a mean/average success rate and need to solve using it? I'm assuming here you mean to find this rate and then apply it?

In the case of an exponential, μ=1/λ so the average time to fail should be 4, correct? So then I would plug that into a Poisson formula to get a solution?

OK, never mind about the Poisson stuff.

Start by considering a simpler case than the one you are given. For example, suppose you ask for the distribution of the number that fail at times between 1 and 2. How would you get that? Now apply the same reasoning to your original problem.

RGV
 

1. What is a joint exponential distribution?

A joint exponential distribution is a probability distribution that describes the likelihood of two or more continuous random variables occurring simultaneously. It is a type of multivariate distribution where each variable follows an exponential distribution.

2. How is a joint exponential distribution different from a single exponential distribution?

A single exponential distribution only describes the probability of one continuous random variable occurring, while a joint exponential distribution describes the probability of multiple variables occurring together. Additionally, in a joint exponential distribution, the variables are often correlated, meaning their values are related in some way.

3. What is the formula for calculating the joint exponential distribution?

The formula for the joint exponential distribution is P(X1=x1, X2=x2, ..., Xn=xn) = λ1e^(-λ1x1) * λ2e^(-λ2x2) * ... * λne^(-λnxn), where X1, X2, ..., Xn are the random variables, λ1, λ2, ..., λn are the rate parameters, and x1, x2, ..., xn are the values at which the variables are being evaluated.

4. How is the correlation between variables represented in a joint exponential distribution?

In a joint exponential distribution, the correlation between variables is represented by the rate parameters. If the rate parameters are equal, the variables are independent and have no correlation. If the rate parameters are different, the variables are correlated, and the strength of the correlation can be determined by the ratio of the rate parameters.

5. What are some real-world applications of the joint exponential distribution?

The joint exponential distribution is commonly used in reliability analysis, where it is used to model the failure times of multiple components in a system. It is also used in queueing theory to model the arrival and service times of customers in a queue. Additionally, the joint exponential distribution has applications in finance, biology, and other fields where the occurrence of multiple events simultaneously needs to be analyzed.

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