Proof that the probability $P(X<Y) = 0.5$ for two iid variableProbability distribution of the sum of products of discrete iid uniform random variablesAny good text on algebra of probability distribution functions?Two iid random variablesMaximum of log-normal random variableProbability of two IID random variablesWhat is the chance that two random binary random variables are independent?Does there exist a probability distribution such that product of two IID variables is a uniform distribution? What about 3 IID variables?What is the distribution of the scalar product of two uniform, iid vectors?Obtaining the probability based on two iid uniform VariableNon-uniform probability distributions that cannot be solved analytically

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Proof that the probability $P(X

Probability distribution of the sum of products of discrete iid uniform random variablesAny good text on algebra of probability distribution functions?Two iid random variablesMaximum of log-normal random variableProbability of two IID random variablesWhat is the chance that two random binary random variables are independent?Does there exist a probability distribution such that product of two IID variables is a uniform distribution? What about 3 IID variables?What is the distribution of the scalar product of two uniform, iid vectors?Obtaining the probability based on two iid uniform VariableNon-uniform probability distributions that cannot be solved analytically













0












$begingroup$


I'm struggling to prove analytically that for two iid (independent and identically distributed) random variables $ P(X<Y) = 0.5$ for any distribution. I'm able to do it by direct integration on specific distributions, but not in the general usecase.










share|cite|improve this question









New contributor




abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$











  • $begingroup$
    It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
    $endgroup$
    – jmerry
    Mar 28 at 19:56










  • $begingroup$
    Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
    $endgroup$
    – abcdaire
    Mar 28 at 20:05










  • $begingroup$
    Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
    $endgroup$
    – Lorenzo
    Mar 28 at 20:25







  • 1




    $begingroup$
    $(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
    $endgroup$
    – Robert Israel
    Mar 28 at 20:30















0












$begingroup$


I'm struggling to prove analytically that for two iid (independent and identically distributed) random variables $ P(X<Y) = 0.5$ for any distribution. I'm able to do it by direct integration on specific distributions, but not in the general usecase.










share|cite|improve this question









New contributor




abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$











  • $begingroup$
    It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
    $endgroup$
    – jmerry
    Mar 28 at 19:56










  • $begingroup$
    Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
    $endgroup$
    – abcdaire
    Mar 28 at 20:05










  • $begingroup$
    Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
    $endgroup$
    – Lorenzo
    Mar 28 at 20:25







  • 1




    $begingroup$
    $(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
    $endgroup$
    – Robert Israel
    Mar 28 at 20:30













0












0








0





$begingroup$


I'm struggling to prove analytically that for two iid (independent and identically distributed) random variables $ P(X<Y) = 0.5$ for any distribution. I'm able to do it by direct integration on specific distributions, but not in the general usecase.










share|cite|improve this question









New contributor




abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$




I'm struggling to prove analytically that for two iid (independent and identically distributed) random variables $ P(X<Y) = 0.5$ for any distribution. I'm able to do it by direct integration on specific distributions, but not in the general usecase.







probability-theory probability-distributions






share|cite|improve this question









New contributor




abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|cite|improve this question









New contributor




abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|cite|improve this question




share|cite|improve this question








edited Mar 28 at 20:18









Max

9851319




9851319






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abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









asked Mar 28 at 19:43









abcdaireabcdaire

101




101




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abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor





abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






abcdaire is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











  • $begingroup$
    It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
    $endgroup$
    – jmerry
    Mar 28 at 19:56










  • $begingroup$
    Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
    $endgroup$
    – abcdaire
    Mar 28 at 20:05










  • $begingroup$
    Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
    $endgroup$
    – Lorenzo
    Mar 28 at 20:25







  • 1




    $begingroup$
    $(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
    $endgroup$
    – Robert Israel
    Mar 28 at 20:30
















  • $begingroup$
    It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
    $endgroup$
    – jmerry
    Mar 28 at 19:56










  • $begingroup$
    Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
    $endgroup$
    – abcdaire
    Mar 28 at 20:05










  • $begingroup$
    Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
    $endgroup$
    – Lorenzo
    Mar 28 at 20:25







  • 1




    $begingroup$
    $(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
    $endgroup$
    – Robert Israel
    Mar 28 at 20:30















$begingroup$
It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
$endgroup$
– jmerry
Mar 28 at 19:56




$begingroup$
It's not true for arbitrary distributions. If there are point masses, such as for any discrete distribution, the nonzero probability that $X=Y$ throws this off. Perhaps there should be additional restrictions?
$endgroup$
– jmerry
Mar 28 at 19:56












$begingroup$
Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
$endgroup$
– abcdaire
Mar 28 at 20:05




$begingroup$
Sorry its in the case of continuous distribution, basically it just come from another thread (that I lost) in the specific case of a continous uniform, and people started arguing that its valid for any distribution (which makes sense by intuiton, symmetry)
$endgroup$
– abcdaire
Mar 28 at 20:05












$begingroup$
Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
$endgroup$
– Lorenzo
Mar 28 at 20:25





$begingroup$
Hint: First show that $P(X = Y) = 0$. To accomplish then, first show that $X,Y$ being iid with a pdf implies that $(X,Y)$ has a pdf. (You can explicitly write down what the pdf is in terms of the pdf of the distribution of $X$.)
$endgroup$
– Lorenzo
Mar 28 at 20:25





1




1




$begingroup$
$(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
$endgroup$
– Robert Israel
Mar 28 at 20:30




$begingroup$
$(x,y) to (y,x)$ is a probability-preserving transformation of the sample space. Thus $P(X>Y) = P(Y>X)$. And if $P(X=Y)=0$ then each must be $1/2$.
$endgroup$
– Robert Israel
Mar 28 at 20:30










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