Is $P(U=u)=1/x$ well defined when $x$ is the values a discrete random variable takes
$begingroup$
If $X$ is a discrete random variable with the following probability distribution function
$P(X=x)=c(x-1),x=2,3,4,5,6$ where $c$ is the normalizing constant.
Now let $U$ be a discrete uniform random variable such that :
$P(U=u)=1/x,u=1,2,dots ,x( x$ is the value taken by $X$)
I was wondering if $P(U=u)$ is well-defined in the sense that $P(U=u)=1/x$ defined above makes sense?
How can the probability that a random variable $U$ take the value $u$ be random?
If I fix $x=2$ then $P(U=1)=P(U=2)=1/2$ , but then for $X=3$ we get $P(U=1)=P(U=2)=P(U=3)=1/3$ . How can we reconcile the two?
Thanks
probability-theory probability-distributions
$endgroup$
add a comment |
$begingroup$
If $X$ is a discrete random variable with the following probability distribution function
$P(X=x)=c(x-1),x=2,3,4,5,6$ where $c$ is the normalizing constant.
Now let $U$ be a discrete uniform random variable such that :
$P(U=u)=1/x,u=1,2,dots ,x( x$ is the value taken by $X$)
I was wondering if $P(U=u)$ is well-defined in the sense that $P(U=u)=1/x$ defined above makes sense?
How can the probability that a random variable $U$ take the value $u$ be random?
If I fix $x=2$ then $P(U=1)=P(U=2)=1/2$ , but then for $X=3$ we get $P(U=1)=P(U=2)=P(U=3)=1/3$ . How can we reconcile the two?
Thanks
probability-theory probability-distributions
$endgroup$
add a comment |
$begingroup$
If $X$ is a discrete random variable with the following probability distribution function
$P(X=x)=c(x-1),x=2,3,4,5,6$ where $c$ is the normalizing constant.
Now let $U$ be a discrete uniform random variable such that :
$P(U=u)=1/x,u=1,2,dots ,x( x$ is the value taken by $X$)
I was wondering if $P(U=u)$ is well-defined in the sense that $P(U=u)=1/x$ defined above makes sense?
How can the probability that a random variable $U$ take the value $u$ be random?
If I fix $x=2$ then $P(U=1)=P(U=2)=1/2$ , but then for $X=3$ we get $P(U=1)=P(U=2)=P(U=3)=1/3$ . How can we reconcile the two?
Thanks
probability-theory probability-distributions
$endgroup$
If $X$ is a discrete random variable with the following probability distribution function
$P(X=x)=c(x-1),x=2,3,4,5,6$ where $c$ is the normalizing constant.
Now let $U$ be a discrete uniform random variable such that :
$P(U=u)=1/x,u=1,2,dots ,x( x$ is the value taken by $X$)
I was wondering if $P(U=u)$ is well-defined in the sense that $P(U=u)=1/x$ defined above makes sense?
How can the probability that a random variable $U$ take the value $u$ be random?
If I fix $x=2$ then $P(U=1)=P(U=2)=1/2$ , but then for $X=3$ we get $P(U=1)=P(U=2)=P(U=3)=1/3$ . How can we reconcile the two?
Thanks
probability-theory probability-distributions
probability-theory probability-distributions
asked Jan 7 at 20:49
user3503589user3503589
1,2351821
1,2351821
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1 Answer
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$begingroup$
I'd read this as a conditional probability, namely: $$P(U=u,|,X=x)=frac 1x$$
Note that, given $X=x$, $U$ is permitted to take $x$ values so the sum of the conditional probabilities is $1$ as it should be.
To gain intuition for the set up, imagine that you first throw a fair die and record its value $X$. Then you toss a coin $X$ times and let $Y$ be the number of Heads you see. Then of course the distribution of $Y$ depends on $X$. For example:$$P(Y=0,|,X=3)=frac 1{2^3}$$ while $$P(Y=0,|,X=6)=frac 1{2^6}$$
and, in general, $$P(Y=0,|,X=x)=frac 1{2^x}$$
$endgroup$
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1 Answer
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1 Answer
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$begingroup$
I'd read this as a conditional probability, namely: $$P(U=u,|,X=x)=frac 1x$$
Note that, given $X=x$, $U$ is permitted to take $x$ values so the sum of the conditional probabilities is $1$ as it should be.
To gain intuition for the set up, imagine that you first throw a fair die and record its value $X$. Then you toss a coin $X$ times and let $Y$ be the number of Heads you see. Then of course the distribution of $Y$ depends on $X$. For example:$$P(Y=0,|,X=3)=frac 1{2^3}$$ while $$P(Y=0,|,X=6)=frac 1{2^6}$$
and, in general, $$P(Y=0,|,X=x)=frac 1{2^x}$$
$endgroup$
add a comment |
$begingroup$
I'd read this as a conditional probability, namely: $$P(U=u,|,X=x)=frac 1x$$
Note that, given $X=x$, $U$ is permitted to take $x$ values so the sum of the conditional probabilities is $1$ as it should be.
To gain intuition for the set up, imagine that you first throw a fair die and record its value $X$. Then you toss a coin $X$ times and let $Y$ be the number of Heads you see. Then of course the distribution of $Y$ depends on $X$. For example:$$P(Y=0,|,X=3)=frac 1{2^3}$$ while $$P(Y=0,|,X=6)=frac 1{2^6}$$
and, in general, $$P(Y=0,|,X=x)=frac 1{2^x}$$
$endgroup$
add a comment |
$begingroup$
I'd read this as a conditional probability, namely: $$P(U=u,|,X=x)=frac 1x$$
Note that, given $X=x$, $U$ is permitted to take $x$ values so the sum of the conditional probabilities is $1$ as it should be.
To gain intuition for the set up, imagine that you first throw a fair die and record its value $X$. Then you toss a coin $X$ times and let $Y$ be the number of Heads you see. Then of course the distribution of $Y$ depends on $X$. For example:$$P(Y=0,|,X=3)=frac 1{2^3}$$ while $$P(Y=0,|,X=6)=frac 1{2^6}$$
and, in general, $$P(Y=0,|,X=x)=frac 1{2^x}$$
$endgroup$
I'd read this as a conditional probability, namely: $$P(U=u,|,X=x)=frac 1x$$
Note that, given $X=x$, $U$ is permitted to take $x$ values so the sum of the conditional probabilities is $1$ as it should be.
To gain intuition for the set up, imagine that you first throw a fair die and record its value $X$. Then you toss a coin $X$ times and let $Y$ be the number of Heads you see. Then of course the distribution of $Y$ depends on $X$. For example:$$P(Y=0,|,X=3)=frac 1{2^3}$$ while $$P(Y=0,|,X=6)=frac 1{2^6}$$
and, in general, $$P(Y=0,|,X=x)=frac 1{2^x}$$
edited Jan 7 at 21:37
answered Jan 7 at 21:18
lulululu
39.8k24778
39.8k24778
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