What is the Cost of Lifetime for an Exponentially Distributed Component?

In summary, the cost of the lifetime of a component is exponentialy distibuted with parameter gamma. When the cost per unit time is a constant, c, the lifetime cost is given by E[cost]=1/gamma. However, if c is not constant, and=c(1-.5e^(a*x) such that a<0. (aka it costs more over time), then the cost in respect to lifetime is given by (-C*gamma^2)*e^(-gamma*x) from 0 to infin.
  • #1
roadrunner
103
0

Homework Statement



component has lifetime X that is exponentialy distibuted with parameter gamma.

a) if the cost per unit time is a constant, c, what is the expectec cost of its lifetime?
b) if c is not constant, and=c(1-.5e^(a*x) such that a<0. (aka it costs more over time) what is its cost inrespect to its lifetime?

Homework Equations



f(x)=gamma*e^(-gamma*x)
E(X)=1/gamma
Var(X)=1/gamma^2


The Attempt at a Solution




i thitnk its just

a) c/gamma
b) (c(1-.5e^(ax)))/gamma

but that seems to easy...
 
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  • #2
To find E[cost] you need to integrate the relevant unit cost (per unit time) weighted by f(x) over the entire domain of X.
 
  • #3
what do you mean "weighted by f(x)"?

so integrate c...so cx

and integrate (c(1-.5e^(ax)))/gamma
which is cx-(.5/a)e^(ax)

where x is time

but don't know what to do now
 
  • #4
Well, you didn't actually integrate "over the entire domain of X"! You found the anti-derivative which is not quite the same thing. For one thing, the "expected value" does not depend upon x. "Weighted by f(x)" means, here, "multiplied by f(x)" before you integrate which is apparently what you did. What is the entire domain of X?
 
  • #5
since x is t then 0 to infinity?
 
  • #6
f(x)=gamma*e^(-gamma*x)

so would i go

c*gamma*e^(-gamma*x)
and
(c(1-.5e^(ax)))/gamma* gamma*e^(-gamma*x)

and then integrate form x=0 to infinity?
 
  • #7
Correct.
 
  • #8
c*gamma*e^(-gamma*x)

becomes (-C*gamma^2)*e^(-gamma*x) from 0 to infin
so at x=infin that equals 0
at x=0, it is -c*gamma^2

so c*gamma^2 is my first answer?

and then (c(1-.5e^(ax)))/gamma* gamma*e^(-gamma*x)
becomes c(1-.5e^(ax)))*e^(-gamma*x)
so C(e^(-gamma*x)-e^(-gamma*x+ax))
which becomes Ce^(-gamma*x)-Ce^(-gamma*x+ax)
and again...Ce^(-gamma*x)-Ce^(x(a-gamma))
so integrating that i get

(-C/gamma)e^(-gamma*x)-(C/(a-gamma)e^(x(a-gamma))) from 0 to infin
again, at infin it equals 0
at 0, the first expression is -C/gamma and the 2nd is C/(a-gamma)
so that means 0-(-C/gamma -C/(a-gamma) )
so my answer is C/gamma +C/(a-gamma)?
 
  • #9
just checked over my mtath and i did them wrong...the first answer is just C

and the 2nd would be C(1-(.5gamma/a-gamma)

NOTE i made a mistake real early...its (c(1-.5e^(ax))) not (c(1-.5e^(ax)))/gamma
and changed that for this above calc
 

1. What is the Exponential distribution?

The Exponential distribution is a probability distribution that describes the time between rare events occurring in a continuous system. It is often used to model the waiting time between arrivals in a Poisson process.

2. How is the Exponential distribution different from other distributions?

The Exponential distribution is unique because it has a constant failure rate, meaning that the probability of an event occurring in a certain time interval does not depend on how much time has passed since the previous event. This is different from other distributions, such as the Normal distribution, which do not have a constant failure rate.

3. What is the formula for the Exponential distribution?

The probability density function for the Exponential distribution is f(x) = λe-λx, where λ is the rate parameter and x is the random variable. The cumulative distribution function is F(x) = 1 - e-λx. These formulas can be used to calculate the probability of a certain event occurring within a given time interval.

4. What are some real-life applications of the Exponential distribution?

The Exponential distribution is commonly used in reliability engineering to model the time between failures of a system. It is also used in queueing theory to model the time between customers arriving at a service facility. Additionally, it has applications in finance, biology, and physics.

5. Can the Exponential distribution be used for both continuous and discrete data?

The Exponential distribution is typically used for continuous data, as it describes the time between events in a continuous system. However, it can also be used for discrete data if the values are rounded to the nearest integer. In this case, the distribution is called the Discrete Exponential distribution.

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