Life of batteries (Probability)

AI Thread Summary
The discussion focuses on finding the cumulative distribution function (CDF) and probability density function (PDF) for the random variable Y, defined as the minimum of two independent exponentially distributed random variables X1 and X2. Participants explore the use of order statistics to derive the PDF, with the formula g(y) = n[(1-Fx(y))^(n-1)]fx(y) being central to the discussion. The correct PDF for Y is determined to be 4e^-4y, following the application of the order statistic technique. The conversation also touches on the use of the Dirac delta function and the relationship between the minimum of two variables and their probabilities. Ultimately, the participants confirm the correct approach and solution for the problem.
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Homework Statement


The lifetime of batteries are independent random variables Xi, i= 1,2,... each having exponential distribution given by the density f(x) = 2e^-2x, x>0, 0 elsewhere.
If a flashlight needs two batteries to work, then the time that the flashlight can operate is a random variable Y=min(X1,X2). Find the CDF and PDF of Y


Homework Equations


Xi, i= 1,2,...
Xi ~ f(x) = 2e^-2x, x>0, 0 elsewhere
Y=min(X1,X2)

The Attempt at a Solution


What I'm not sure about is the initial approach to this problem. I'm not sure if I should treat it as an order statistics problem in which case i'd be trying to use the equation:

g(y) = n((1-Fx(y))^(n-1))f(y)

though I'm not sure if Fx(y) and f(y) are just the respective cdf and pdf of X.
I know if i have the pdf, I can get the cdf easily.
 
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Do you know what a Dirac delta function is? Given a PDF f(x1,x2) for two variables x1 and x2, the PDF g(y) for any function y=h(x1,x2) of these is given by
g(y) = \int dx_1\,dx_2\,f(x_1,x_2)\delta(y-h(x_1,x_2))
where \delta(y) is the Dirac delta function, and where x1 and x2 are integrated over their complete ranges (whatever they are).
 
Thanks for the help. I don't recall covering the dirac delta at all but I'm trying to use it and we'll see how it works. I was thinking there might be a transform involved as well? Thanks again, I appreciate it.
 
I was able to use this method to solve the problem, but it's tricky. There's probably (no pun intended) a method that avoids it; maybe someone else can help.
 
Since Y is min(X1, X2), and if Prob{Xi < x) = F(x), what is the probability Prob{min(X1,X2) < y), in terms of the F function?
 
I just can't figure out how to deal with that "min". I know that if say Y=X1^2 then I would just follow:

cdf = Fy(y) = P(Y<y) = P(X^2<y) = P(X<Y^1/2) etc, but I don't know how to represent that 'minimum' when I'm trying to solve the inequality for X.

Thanks again.
 
If the minimum of X1 and X2 is less than y, then it cannot be the case that "both X1 > y and X2 > y."
 
Would it correct to say then P(Y1>y) = P(X1>Y)*P(X2>Y) ?
Thanks.
 
Exactly, or P(Y<y) = 1 - (1-P(X1<Y))*(1-P(X2<Y)).
 
  • #10
Ok, I'm coming up with a pdf of:

(4e^-2y)*(1+e^-2y)

OR

(4e^-2y)+(4e^-4y)

Thoughts?
 
  • #11
Not what I got. And I got the same answer with my fancy delta-function method and EmulaFish's simpler method.

Can you show your work?
 
  • #12
well after talking to my professor, he said I should be using the order statistic technique

pdf of Y=min(X1,X2...Xn) = g(y) = n*[(1-Fx(y))^n-1]*fx(y)

where n=2 for this case
Fx(y) is cdf of X evaluated at y = -e^-2y
fx(y) is pdf of X evaluated at y = 2e^-2y

I put those into g(y) and that's how I came up with that.
Thanks again.
 
  • #13
Any ideas on what I might be doing wrong? Thanks.
 
  • #14
I figured out one thing I've been doing wrong. My new answer for pdf of Y=min(X1,X2) is

4e^-4y

I'm pretty confident about that one.
 
  • #15
That's what I got!
 

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