Calculating the Central Density Function for a Continuous Random Variable

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The discussion centers on calculating the cumulative distribution function (CDF) for a continuous random variable defined by the probability density function (PDF) f(x) = (λ/2)e^{-λ(x-θ)} for x > θ. The initial attempt at integrating the PDF resulted in an incorrect limit leading to infinity, prompting a reevaluation of the lower limit of integration. It was clarified that the lower limit should be θ, as the PDF is zero for x ≤ θ. After correcting the integration limits and notation, the CDF was recalculated, leading to a valid expression. The conversation emphasizes the importance of proper limits in integration for accurate results in probability calculations.
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Homework Statement

-infinity<x<infinity
x> theta
f(x) = \frac{\lambda}{2}e^{-\lambda (x-\theta)}

F(x) = \int_{-\infty}^x f(x) dx

Homework Equations


The Attempt at a Solution


Homework Statement



\int \frac{\lambda}{2}e^{-\lambda (x-\theta)} dx
= -\frac{1}{2}e^{-\lambda(x-\theta)}

Insert the limits:
-\frac{1}{2}e^{-\lambda(x-\theta)} + \frac{1}{2}e^{-\lambda(-\infty-\theta)}

= infinity.

The last part should not be infinity so can anyone see where I go wrong?
 
Last edited:
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You have defined f(x) for x&gt;\theta. Is it zero elsewhere? Should your lower limit be \theta?
 
Yes it should! Thanks

Insert the limits:
-\frac{1}{2}e^{-\lambda(x-\theta)} + \frac{1}{2}e^{-\lambda(\theta-\theta)}
=
1/2 -\frac{1}{2}e^{-\lambda(x-\theta)}
 
Last edited:
You mean e^{-\lambda(\theta- \theta)}, not e^{-\lambda(-\theta- \theta)}
 
Thanks, corrected it now.
 
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