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
$begingroup$
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.
probability-theory probability-distributions
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.
$endgroup$
add a comment |
$begingroup$
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.
probability-theory probability-distributions
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
add a comment |
$begingroup$
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.
probability-theory probability-distributions
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
probability-theory probability-distributions
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.
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.
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.
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asked Mar 28 at 19:43
abcdaireabcdaire
101
101
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.
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
add a comment |
$begingroup$
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
add a comment |
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$begingroup$
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