PDF of an exponential distribution with varying paramter, lambda












1














Suppose that the lifetime of a device is exponential with rate λ, but suppose
also that the value of λ is not fixed but is itself a random variable that is
uniform in the range [a, b) with 0 < a.



Can the pdf of the lifetime of the device be written as:



$ lambda = begin{cases} 0, x < a\ dfrac {1} {b-a} , aleq x < b\ 0, xgeq b end{cases} $










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  • 1




    The PDF of $lambda$, or the PDF of the lifetime of the device?
    – Clarinetist
    Nov 30 '18 at 1:12










  • If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
    – gd1035
    Nov 30 '18 at 1:18










  • What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
    – BlackMath
    Nov 30 '18 at 6:06


















1














Suppose that the lifetime of a device is exponential with rate λ, but suppose
also that the value of λ is not fixed but is itself a random variable that is
uniform in the range [a, b) with 0 < a.



Can the pdf of the lifetime of the device be written as:



$ lambda = begin{cases} 0, x < a\ dfrac {1} {b-a} , aleq x < b\ 0, xgeq b end{cases} $










share|cite|improve this question




















  • 1




    The PDF of $lambda$, or the PDF of the lifetime of the device?
    – Clarinetist
    Nov 30 '18 at 1:12










  • If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
    – gd1035
    Nov 30 '18 at 1:18










  • What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
    – BlackMath
    Nov 30 '18 at 6:06
















1












1








1







Suppose that the lifetime of a device is exponential with rate λ, but suppose
also that the value of λ is not fixed but is itself a random variable that is
uniform in the range [a, b) with 0 < a.



Can the pdf of the lifetime of the device be written as:



$ lambda = begin{cases} 0, x < a\ dfrac {1} {b-a} , aleq x < b\ 0, xgeq b end{cases} $










share|cite|improve this question















Suppose that the lifetime of a device is exponential with rate λ, but suppose
also that the value of λ is not fixed but is itself a random variable that is
uniform in the range [a, b) with 0 < a.



Can the pdf of the lifetime of the device be written as:



$ lambda = begin{cases} 0, x < a\ dfrac {1} {b-a} , aleq x < b\ 0, xgeq b end{cases} $







probability uniform-distribution exponential-distribution






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share|cite|improve this question













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edited Nov 30 '18 at 1:25







Basileus

















asked Nov 30 '18 at 1:11









BasileusBasileus

113




113








  • 1




    The PDF of $lambda$, or the PDF of the lifetime of the device?
    – Clarinetist
    Nov 30 '18 at 1:12










  • If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
    – gd1035
    Nov 30 '18 at 1:18










  • What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
    – BlackMath
    Nov 30 '18 at 6:06
















  • 1




    The PDF of $lambda$, or the PDF of the lifetime of the device?
    – Clarinetist
    Nov 30 '18 at 1:12










  • If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
    – gd1035
    Nov 30 '18 at 1:18










  • What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
    – BlackMath
    Nov 30 '18 at 6:06










1




1




The PDF of $lambda$, or the PDF of the lifetime of the device?
– Clarinetist
Nov 30 '18 at 1:12




The PDF of $lambda$, or the PDF of the lifetime of the device?
– Clarinetist
Nov 30 '18 at 1:12












If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
– gd1035
Nov 30 '18 at 1:18




If $lambda$ is Uniform on $[a,b)$, then the PDF would be 0 for $xgeq b$.
– gd1035
Nov 30 '18 at 1:18












What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
– BlackMath
Nov 30 '18 at 6:06






What you wrote is the PDF of the random variable $lambda$, not the PDF of the device lifetime.
– BlackMath
Nov 30 '18 at 6:06












1 Answer
1






active

oldest

votes


















1














Let $T|lambda sim text{Exp}(lambda)$ be the lifetime of the device and let $lambda sim text{U}[a,b)$ be the parameter. Then for all $t geqslant 0$ you have:



$$begin{equation} begin{aligned}
f_T(t)
&= int limits_a^b p(T=t|lambda) pi(lambda) dlambda \[6pt]
&= frac{1}{a-b} int limits_a^b exp(-lambda t) dlambda \[6pt]
&= frac{1}{a-b} Bigg[ - frac{1}{t} cdot exp(-lambda t) Bigg]_{lambda=a}^{lambda=b} \[6pt]
&= frac{1}{a-b} Bigg[ frac{exp(-at) - exp(-bt)}{t} Bigg] \[6pt]
&= frac{e^{-at} - e^{-bt}}{a-b} cdot frac{1}{t}. \[6pt]
end{aligned} end{equation}$$






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  • So, this has to take into account the law of total probability, thanks for that.
    – Basileus
    Nov 30 '18 at 13:09










  • Yes, the first step is an application of that law.
    – Ben
    Nov 30 '18 at 22:51











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1 Answer
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1














Let $T|lambda sim text{Exp}(lambda)$ be the lifetime of the device and let $lambda sim text{U}[a,b)$ be the parameter. Then for all $t geqslant 0$ you have:



$$begin{equation} begin{aligned}
f_T(t)
&= int limits_a^b p(T=t|lambda) pi(lambda) dlambda \[6pt]
&= frac{1}{a-b} int limits_a^b exp(-lambda t) dlambda \[6pt]
&= frac{1}{a-b} Bigg[ - frac{1}{t} cdot exp(-lambda t) Bigg]_{lambda=a}^{lambda=b} \[6pt]
&= frac{1}{a-b} Bigg[ frac{exp(-at) - exp(-bt)}{t} Bigg] \[6pt]
&= frac{e^{-at} - e^{-bt}}{a-b} cdot frac{1}{t}. \[6pt]
end{aligned} end{equation}$$






share|cite|improve this answer





















  • So, this has to take into account the law of total probability, thanks for that.
    – Basileus
    Nov 30 '18 at 13:09










  • Yes, the first step is an application of that law.
    – Ben
    Nov 30 '18 at 22:51
















1














Let $T|lambda sim text{Exp}(lambda)$ be the lifetime of the device and let $lambda sim text{U}[a,b)$ be the parameter. Then for all $t geqslant 0$ you have:



$$begin{equation} begin{aligned}
f_T(t)
&= int limits_a^b p(T=t|lambda) pi(lambda) dlambda \[6pt]
&= frac{1}{a-b} int limits_a^b exp(-lambda t) dlambda \[6pt]
&= frac{1}{a-b} Bigg[ - frac{1}{t} cdot exp(-lambda t) Bigg]_{lambda=a}^{lambda=b} \[6pt]
&= frac{1}{a-b} Bigg[ frac{exp(-at) - exp(-bt)}{t} Bigg] \[6pt]
&= frac{e^{-at} - e^{-bt}}{a-b} cdot frac{1}{t}. \[6pt]
end{aligned} end{equation}$$






share|cite|improve this answer





















  • So, this has to take into account the law of total probability, thanks for that.
    – Basileus
    Nov 30 '18 at 13:09










  • Yes, the first step is an application of that law.
    – Ben
    Nov 30 '18 at 22:51














1












1








1






Let $T|lambda sim text{Exp}(lambda)$ be the lifetime of the device and let $lambda sim text{U}[a,b)$ be the parameter. Then for all $t geqslant 0$ you have:



$$begin{equation} begin{aligned}
f_T(t)
&= int limits_a^b p(T=t|lambda) pi(lambda) dlambda \[6pt]
&= frac{1}{a-b} int limits_a^b exp(-lambda t) dlambda \[6pt]
&= frac{1}{a-b} Bigg[ - frac{1}{t} cdot exp(-lambda t) Bigg]_{lambda=a}^{lambda=b} \[6pt]
&= frac{1}{a-b} Bigg[ frac{exp(-at) - exp(-bt)}{t} Bigg] \[6pt]
&= frac{e^{-at} - e^{-bt}}{a-b} cdot frac{1}{t}. \[6pt]
end{aligned} end{equation}$$






share|cite|improve this answer












Let $T|lambda sim text{Exp}(lambda)$ be the lifetime of the device and let $lambda sim text{U}[a,b)$ be the parameter. Then for all $t geqslant 0$ you have:



$$begin{equation} begin{aligned}
f_T(t)
&= int limits_a^b p(T=t|lambda) pi(lambda) dlambda \[6pt]
&= frac{1}{a-b} int limits_a^b exp(-lambda t) dlambda \[6pt]
&= frac{1}{a-b} Bigg[ - frac{1}{t} cdot exp(-lambda t) Bigg]_{lambda=a}^{lambda=b} \[6pt]
&= frac{1}{a-b} Bigg[ frac{exp(-at) - exp(-bt)}{t} Bigg] \[6pt]
&= frac{e^{-at} - e^{-bt}}{a-b} cdot frac{1}{t}. \[6pt]
end{aligned} end{equation}$$







share|cite|improve this answer












share|cite|improve this answer



share|cite|improve this answer










answered Nov 30 '18 at 5:45









BenBen

92611




92611












  • So, this has to take into account the law of total probability, thanks for that.
    – Basileus
    Nov 30 '18 at 13:09










  • Yes, the first step is an application of that law.
    – Ben
    Nov 30 '18 at 22:51


















  • So, this has to take into account the law of total probability, thanks for that.
    – Basileus
    Nov 30 '18 at 13:09










  • Yes, the first step is an application of that law.
    – Ben
    Nov 30 '18 at 22:51
















So, this has to take into account the law of total probability, thanks for that.
– Basileus
Nov 30 '18 at 13:09




So, this has to take into account the law of total probability, thanks for that.
– Basileus
Nov 30 '18 at 13:09












Yes, the first step is an application of that law.
– Ben
Nov 30 '18 at 22:51




Yes, the first step is an application of that law.
– Ben
Nov 30 '18 at 22:51


















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