Compute the probability $P(X ≤ Y|min{X,Y}=x)$












1












$begingroup$


Let $X$ and $Y$ be two independent exponential random variables with parameters $lambda$ and $mu$, respectively. Compute the probability $P(X ≤ Y|min{X,Y}=x)$.



Here is what I did:

If $Y=min{X,Y}$, then $Ylt X$, $P(X ≤ Y|min{X,Y}=x)=0$.

If $X=min{X,Y}$, then $Xlt Y$,
$$
begin{aligned}
P(X ≤ Y|min{X,Y}=x)&=P(X ≤ Y|X=x)\
&=frac{P(Ygt x)}{f_X(x)}\
&=frac{1-P(Ylt x)}{f_X(x)}\
&={1-int_{0}^xmu e^{-mu y}dyover lambda e^{-lambda x}}
end{aligned}
$$



Am I right?










share|cite|improve this question











$endgroup$












  • $begingroup$
    No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
    $endgroup$
    – Xiaomi
    Oct 1 '18 at 1:45










  • $begingroup$
    @Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:52










  • $begingroup$
    @YibeiHe, still no. $f_X(x) ne P(X=x)$
    $endgroup$
    – Zamarion
    Oct 1 '18 at 1:54


















1












$begingroup$


Let $X$ and $Y$ be two independent exponential random variables with parameters $lambda$ and $mu$, respectively. Compute the probability $P(X ≤ Y|min{X,Y}=x)$.



Here is what I did:

If $Y=min{X,Y}$, then $Ylt X$, $P(X ≤ Y|min{X,Y}=x)=0$.

If $X=min{X,Y}$, then $Xlt Y$,
$$
begin{aligned}
P(X ≤ Y|min{X,Y}=x)&=P(X ≤ Y|X=x)\
&=frac{P(Ygt x)}{f_X(x)}\
&=frac{1-P(Ylt x)}{f_X(x)}\
&={1-int_{0}^xmu e^{-mu y}dyover lambda e^{-lambda x}}
end{aligned}
$$



Am I right?










share|cite|improve this question











$endgroup$












  • $begingroup$
    No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
    $endgroup$
    – Xiaomi
    Oct 1 '18 at 1:45










  • $begingroup$
    @Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:52










  • $begingroup$
    @YibeiHe, still no. $f_X(x) ne P(X=x)$
    $endgroup$
    – Zamarion
    Oct 1 '18 at 1:54
















1












1








1


3



$begingroup$


Let $X$ and $Y$ be two independent exponential random variables with parameters $lambda$ and $mu$, respectively. Compute the probability $P(X ≤ Y|min{X,Y}=x)$.



Here is what I did:

If $Y=min{X,Y}$, then $Ylt X$, $P(X ≤ Y|min{X,Y}=x)=0$.

If $X=min{X,Y}$, then $Xlt Y$,
$$
begin{aligned}
P(X ≤ Y|min{X,Y}=x)&=P(X ≤ Y|X=x)\
&=frac{P(Ygt x)}{f_X(x)}\
&=frac{1-P(Ylt x)}{f_X(x)}\
&={1-int_{0}^xmu e^{-mu y}dyover lambda e^{-lambda x}}
end{aligned}
$$



Am I right?










share|cite|improve this question











$endgroup$




Let $X$ and $Y$ be two independent exponential random variables with parameters $lambda$ and $mu$, respectively. Compute the probability $P(X ≤ Y|min{X,Y}=x)$.



Here is what I did:

If $Y=min{X,Y}$, then $Ylt X$, $P(X ≤ Y|min{X,Y}=x)=0$.

If $X=min{X,Y}$, then $Xlt Y$,
$$
begin{aligned}
P(X ≤ Y|min{X,Y}=x)&=P(X ≤ Y|X=x)\
&=frac{P(Ygt x)}{f_X(x)}\
&=frac{1-P(Ylt x)}{f_X(x)}\
&={1-int_{0}^xmu e^{-mu y}dyover lambda e^{-lambda x}}
end{aligned}
$$



Am I right?







probability






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Oct 1 '18 at 1:51







Yibei He

















asked Oct 1 '18 at 1:29









Yibei HeYibei He

3139




3139












  • $begingroup$
    No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
    $endgroup$
    – Xiaomi
    Oct 1 '18 at 1:45










  • $begingroup$
    @Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:52










  • $begingroup$
    @YibeiHe, still no. $f_X(x) ne P(X=x)$
    $endgroup$
    – Zamarion
    Oct 1 '18 at 1:54




















  • $begingroup$
    No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
    $endgroup$
    – Xiaomi
    Oct 1 '18 at 1:45










  • $begingroup$
    @Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:52










  • $begingroup$
    @YibeiHe, still no. $f_X(x) ne P(X=x)$
    $endgroup$
    – Zamarion
    Oct 1 '18 at 1:54


















$begingroup$
No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
$endgroup$
– Xiaomi
Oct 1 '18 at 1:45




$begingroup$
No. You should answer in a single statement, not a case by case basis. Secondly, your proof is wrong because $P(X = x)$ is $0$ since $X$ is a continuous random variable, so you're dividing by zero
$endgroup$
– Xiaomi
Oct 1 '18 at 1:45












$begingroup$
@Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
$endgroup$
– Yibei He
Oct 1 '18 at 1:52




$begingroup$
@Xiaomi I improved my answer. Is that correct now? And how to answer in a single statement?
$endgroup$
– Yibei He
Oct 1 '18 at 1:52












$begingroup$
@YibeiHe, still no. $f_X(x) ne P(X=x)$
$endgroup$
– Zamarion
Oct 1 '18 at 1:54






$begingroup$
@YibeiHe, still no. $f_X(x) ne P(X=x)$
$endgroup$
– Zamarion
Oct 1 '18 at 1:54












2 Answers
2






active

oldest

votes


















1












$begingroup$

$$P(X le Y mid min{X, Y} = x) = frac{P(X le Y, min{X,Y} in [x, x+dx])}{P(min{X,Y} in [x, x+dx])}.$$



The numerator is equal to
$$P(X le Y, X in [x, x + dx]) = P(X in [x, x+dx]) P(Y ge x) = (lambda e^{-lambda x} , dx)(e^{-mu x}).$$



The denominator is equal to
$$P(X le Y, X in [x, x + dx]) + P(X ge Y, Y in [x, x + dx])
= (lambda e^{-lambda x} , dx)(e^{-mu x}) + (mu e^{-mu x} , dx) (e^{-lambda x})$$

by similar computations.



Thus the answer is $frac{lambda}{lambda + mu}$. Note that it is interesting that the answer does not depend on $x$. [You could also quickly answer this question using properties of Poisson processes, but perhaps this is off-topic.]





Response to comment: It actually does work. $$F(x + dx) - F(x) = e^{-lambda x} - e^{-lambda (x + dx)} = e^{-lambda x}(1 - e^{-lambda , dx}).$$
Then using $e^z = 1 + z + O(z^2)$, we can plug in $e^{-lambda dx} = 1 - lambda , dx$ to obtain $lambda e^{-lambda x} , dx$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 2:28










  • $begingroup$
    @YibeiHe See my edit.
    $endgroup$
    – angryavian
    Oct 1 '18 at 5:56



















1












$begingroup$

The second part isn't quite right, since $P(X=x)=0$ for any continuous random variable $X$.



You can compute this probability that way,

begin{align}
P(X<Y|X=x) &= P(x<Y) \
&= 1 - int_0^xmu e^{-mu y}dy \
&= 1 - (1-e^{-mu x}) \
&= e^{mu x}
end{align}



Note that this is not the final answer and you should be careful on how you have split your two cases.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Shall I divide it by the pdf of X since the condition is X=x.
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:54











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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









1












$begingroup$

$$P(X le Y mid min{X, Y} = x) = frac{P(X le Y, min{X,Y} in [x, x+dx])}{P(min{X,Y} in [x, x+dx])}.$$



The numerator is equal to
$$P(X le Y, X in [x, x + dx]) = P(X in [x, x+dx]) P(Y ge x) = (lambda e^{-lambda x} , dx)(e^{-mu x}).$$



The denominator is equal to
$$P(X le Y, X in [x, x + dx]) + P(X ge Y, Y in [x, x + dx])
= (lambda e^{-lambda x} , dx)(e^{-mu x}) + (mu e^{-mu x} , dx) (e^{-lambda x})$$

by similar computations.



Thus the answer is $frac{lambda}{lambda + mu}$. Note that it is interesting that the answer does not depend on $x$. [You could also quickly answer this question using properties of Poisson processes, but perhaps this is off-topic.]





Response to comment: It actually does work. $$F(x + dx) - F(x) = e^{-lambda x} - e^{-lambda (x + dx)} = e^{-lambda x}(1 - e^{-lambda , dx}).$$
Then using $e^z = 1 + z + O(z^2)$, we can plug in $e^{-lambda dx} = 1 - lambda , dx$ to obtain $lambda e^{-lambda x} , dx$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 2:28










  • $begingroup$
    @YibeiHe See my edit.
    $endgroup$
    – angryavian
    Oct 1 '18 at 5:56
















1












$begingroup$

$$P(X le Y mid min{X, Y} = x) = frac{P(X le Y, min{X,Y} in [x, x+dx])}{P(min{X,Y} in [x, x+dx])}.$$



The numerator is equal to
$$P(X le Y, X in [x, x + dx]) = P(X in [x, x+dx]) P(Y ge x) = (lambda e^{-lambda x} , dx)(e^{-mu x}).$$



The denominator is equal to
$$P(X le Y, X in [x, x + dx]) + P(X ge Y, Y in [x, x + dx])
= (lambda e^{-lambda x} , dx)(e^{-mu x}) + (mu e^{-mu x} , dx) (e^{-lambda x})$$

by similar computations.



Thus the answer is $frac{lambda}{lambda + mu}$. Note that it is interesting that the answer does not depend on $x$. [You could also quickly answer this question using properties of Poisson processes, but perhaps this is off-topic.]





Response to comment: It actually does work. $$F(x + dx) - F(x) = e^{-lambda x} - e^{-lambda (x + dx)} = e^{-lambda x}(1 - e^{-lambda , dx}).$$
Then using $e^z = 1 + z + O(z^2)$, we can plug in $e^{-lambda dx} = 1 - lambda , dx$ to obtain $lambda e^{-lambda x} , dx$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 2:28










  • $begingroup$
    @YibeiHe See my edit.
    $endgroup$
    – angryavian
    Oct 1 '18 at 5:56














1












1








1





$begingroup$

$$P(X le Y mid min{X, Y} = x) = frac{P(X le Y, min{X,Y} in [x, x+dx])}{P(min{X,Y} in [x, x+dx])}.$$



The numerator is equal to
$$P(X le Y, X in [x, x + dx]) = P(X in [x, x+dx]) P(Y ge x) = (lambda e^{-lambda x} , dx)(e^{-mu x}).$$



The denominator is equal to
$$P(X le Y, X in [x, x + dx]) + P(X ge Y, Y in [x, x + dx])
= (lambda e^{-lambda x} , dx)(e^{-mu x}) + (mu e^{-mu x} , dx) (e^{-lambda x})$$

by similar computations.



Thus the answer is $frac{lambda}{lambda + mu}$. Note that it is interesting that the answer does not depend on $x$. [You could also quickly answer this question using properties of Poisson processes, but perhaps this is off-topic.]





Response to comment: It actually does work. $$F(x + dx) - F(x) = e^{-lambda x} - e^{-lambda (x + dx)} = e^{-lambda x}(1 - e^{-lambda , dx}).$$
Then using $e^z = 1 + z + O(z^2)$, we can plug in $e^{-lambda dx} = 1 - lambda , dx$ to obtain $lambda e^{-lambda x} , dx$.






share|cite|improve this answer











$endgroup$



$$P(X le Y mid min{X, Y} = x) = frac{P(X le Y, min{X,Y} in [x, x+dx])}{P(min{X,Y} in [x, x+dx])}.$$



The numerator is equal to
$$P(X le Y, X in [x, x + dx]) = P(X in [x, x+dx]) P(Y ge x) = (lambda e^{-lambda x} , dx)(e^{-mu x}).$$



The denominator is equal to
$$P(X le Y, X in [x, x + dx]) + P(X ge Y, Y in [x, x + dx])
= (lambda e^{-lambda x} , dx)(e^{-mu x}) + (mu e^{-mu x} , dx) (e^{-lambda x})$$

by similar computations.



Thus the answer is $frac{lambda}{lambda + mu}$. Note that it is interesting that the answer does not depend on $x$. [You could also quickly answer this question using properties of Poisson processes, but perhaps this is off-topic.]





Response to comment: It actually does work. $$F(x + dx) - F(x) = e^{-lambda x} - e^{-lambda (x + dx)} = e^{-lambda x}(1 - e^{-lambda , dx}).$$
Then using $e^z = 1 + z + O(z^2)$, we can plug in $e^{-lambda dx} = 1 - lambda , dx$ to obtain $lambda e^{-lambda x} , dx$.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited Oct 1 '18 at 5:56

























answered Oct 1 '18 at 1:55









angryavianangryavian

42.1k23381




42.1k23381












  • $begingroup$
    Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 2:28










  • $begingroup$
    @YibeiHe See my edit.
    $endgroup$
    – angryavian
    Oct 1 '18 at 5:56


















  • $begingroup$
    Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
    $endgroup$
    – Yibei He
    Oct 1 '18 at 2:28










  • $begingroup$
    @YibeiHe See my edit.
    $endgroup$
    – angryavian
    Oct 1 '18 at 5:56
















$begingroup$
Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
$endgroup$
– Yibei He
Oct 1 '18 at 2:28




$begingroup$
Thanks a lot! One question: if I do the part P(x=[x,x+dx]) into F(x+dx)-F(x), it doesn’t work. Why?
$endgroup$
– Yibei He
Oct 1 '18 at 2:28












$begingroup$
@YibeiHe See my edit.
$endgroup$
– angryavian
Oct 1 '18 at 5:56




$begingroup$
@YibeiHe See my edit.
$endgroup$
– angryavian
Oct 1 '18 at 5:56











1












$begingroup$

The second part isn't quite right, since $P(X=x)=0$ for any continuous random variable $X$.



You can compute this probability that way,

begin{align}
P(X<Y|X=x) &= P(x<Y) \
&= 1 - int_0^xmu e^{-mu y}dy \
&= 1 - (1-e^{-mu x}) \
&= e^{mu x}
end{align}



Note that this is not the final answer and you should be careful on how you have split your two cases.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Shall I divide it by the pdf of X since the condition is X=x.
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:54
















1












$begingroup$

The second part isn't quite right, since $P(X=x)=0$ for any continuous random variable $X$.



You can compute this probability that way,

begin{align}
P(X<Y|X=x) &= P(x<Y) \
&= 1 - int_0^xmu e^{-mu y}dy \
&= 1 - (1-e^{-mu x}) \
&= e^{mu x}
end{align}



Note that this is not the final answer and you should be careful on how you have split your two cases.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Shall I divide it by the pdf of X since the condition is X=x.
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:54














1












1








1





$begingroup$

The second part isn't quite right, since $P(X=x)=0$ for any continuous random variable $X$.



You can compute this probability that way,

begin{align}
P(X<Y|X=x) &= P(x<Y) \
&= 1 - int_0^xmu e^{-mu y}dy \
&= 1 - (1-e^{-mu x}) \
&= e^{mu x}
end{align}



Note that this is not the final answer and you should be careful on how you have split your two cases.






share|cite|improve this answer











$endgroup$



The second part isn't quite right, since $P(X=x)=0$ for any continuous random variable $X$.



You can compute this probability that way,

begin{align}
P(X<Y|X=x) &= P(x<Y) \
&= 1 - int_0^xmu e^{-mu y}dy \
&= 1 - (1-e^{-mu x}) \
&= e^{mu x}
end{align}



Note that this is not the final answer and you should be careful on how you have split your two cases.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited Oct 1 '18 at 2:28

























answered Oct 1 '18 at 1:47









ZamarionZamarion

33719




33719












  • $begingroup$
    Shall I divide it by the pdf of X since the condition is X=x.
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:54


















  • $begingroup$
    Shall I divide it by the pdf of X since the condition is X=x.
    $endgroup$
    – Yibei He
    Oct 1 '18 at 1:54
















$begingroup$
Shall I divide it by the pdf of X since the condition is X=x.
$endgroup$
– Yibei He
Oct 1 '18 at 1:54




$begingroup$
Shall I divide it by the pdf of X since the condition is X=x.
$endgroup$
– Yibei He
Oct 1 '18 at 1:54


















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