Clarification on a probability theory statement
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In a text I'm reading, it says the following:
Suppose that $(Omega, mathcal{F})$ is a measureable space and that $X_1, dots, X_n: Omega to mathbf{R}$ are $n$ (real-valued) random variables. If $f: mathbf{R}^n to mathbf{R}$ is a measurable function, then $Z = f(X_1, dots, X_n)$ is measurable with respect to the $sigma$-algebra $sigma(X_1, dots, X_n)$.
I guess my first question is what does it mean for the random variable $Z$ to be measurable with respect to $sigma(X_1, dots, X_n)$? Does it mean that we are considering $Z(omega) = f(X_1(omega), dots, X_n(omega))$ as a map $Z: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}))$?
If so, in this case I think we can do it in two steps: first, we show that $X: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}^n))$ is measurable, and then we apply the result that $Z = f circ X$, the composition of measurable maps, hence measurable.
That $X$ as defined above (the vector random variable) is measurable is clear. Let $B = B_1 times cdots B_n$, where $B_i in mathcal{B}(mathbf{R})$ for all $i$. Note that
$$
X^{-1}(B) = cap_{i=1}^n X_i^{-1}(B_i) in sigma(X_1, dots, X_n),
$$
where the membership follows since for each $i$, $X_i^{-1}(B_i) in sigma(X_i) subset sigma(X_1, dots X_n)$, and latter is closed under intersection. But then now use that the Borels on $mathbf{R}^n$ are generated by these `rectangular' sets, so hence $X$ is measurable. Is this roughly the idea?
probability-theory measure-theory
$endgroup$
add a comment |
$begingroup$
In a text I'm reading, it says the following:
Suppose that $(Omega, mathcal{F})$ is a measureable space and that $X_1, dots, X_n: Omega to mathbf{R}$ are $n$ (real-valued) random variables. If $f: mathbf{R}^n to mathbf{R}$ is a measurable function, then $Z = f(X_1, dots, X_n)$ is measurable with respect to the $sigma$-algebra $sigma(X_1, dots, X_n)$.
I guess my first question is what does it mean for the random variable $Z$ to be measurable with respect to $sigma(X_1, dots, X_n)$? Does it mean that we are considering $Z(omega) = f(X_1(omega), dots, X_n(omega))$ as a map $Z: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}))$?
If so, in this case I think we can do it in two steps: first, we show that $X: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}^n))$ is measurable, and then we apply the result that $Z = f circ X$, the composition of measurable maps, hence measurable.
That $X$ as defined above (the vector random variable) is measurable is clear. Let $B = B_1 times cdots B_n$, where $B_i in mathcal{B}(mathbf{R})$ for all $i$. Note that
$$
X^{-1}(B) = cap_{i=1}^n X_i^{-1}(B_i) in sigma(X_1, dots, X_n),
$$
where the membership follows since for each $i$, $X_i^{-1}(B_i) in sigma(X_i) subset sigma(X_1, dots X_n)$, and latter is closed under intersection. But then now use that the Borels on $mathbf{R}^n$ are generated by these `rectangular' sets, so hence $X$ is measurable. Is this roughly the idea?
probability-theory measure-theory
$endgroup$
$begingroup$
Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27
add a comment |
$begingroup$
In a text I'm reading, it says the following:
Suppose that $(Omega, mathcal{F})$ is a measureable space and that $X_1, dots, X_n: Omega to mathbf{R}$ are $n$ (real-valued) random variables. If $f: mathbf{R}^n to mathbf{R}$ is a measurable function, then $Z = f(X_1, dots, X_n)$ is measurable with respect to the $sigma$-algebra $sigma(X_1, dots, X_n)$.
I guess my first question is what does it mean for the random variable $Z$ to be measurable with respect to $sigma(X_1, dots, X_n)$? Does it mean that we are considering $Z(omega) = f(X_1(omega), dots, X_n(omega))$ as a map $Z: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}))$?
If so, in this case I think we can do it in two steps: first, we show that $X: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}^n))$ is measurable, and then we apply the result that $Z = f circ X$, the composition of measurable maps, hence measurable.
That $X$ as defined above (the vector random variable) is measurable is clear. Let $B = B_1 times cdots B_n$, where $B_i in mathcal{B}(mathbf{R})$ for all $i$. Note that
$$
X^{-1}(B) = cap_{i=1}^n X_i^{-1}(B_i) in sigma(X_1, dots, X_n),
$$
where the membership follows since for each $i$, $X_i^{-1}(B_i) in sigma(X_i) subset sigma(X_1, dots X_n)$, and latter is closed under intersection. But then now use that the Borels on $mathbf{R}^n$ are generated by these `rectangular' sets, so hence $X$ is measurable. Is this roughly the idea?
probability-theory measure-theory
$endgroup$
In a text I'm reading, it says the following:
Suppose that $(Omega, mathcal{F})$ is a measureable space and that $X_1, dots, X_n: Omega to mathbf{R}$ are $n$ (real-valued) random variables. If $f: mathbf{R}^n to mathbf{R}$ is a measurable function, then $Z = f(X_1, dots, X_n)$ is measurable with respect to the $sigma$-algebra $sigma(X_1, dots, X_n)$.
I guess my first question is what does it mean for the random variable $Z$ to be measurable with respect to $sigma(X_1, dots, X_n)$? Does it mean that we are considering $Z(omega) = f(X_1(omega), dots, X_n(omega))$ as a map $Z: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}))$?
If so, in this case I think we can do it in two steps: first, we show that $X: (Omega, sigma(X_1, dots, X_n)) to (mathbf{R}, mathcal{B}(mathbf{R}^n))$ is measurable, and then we apply the result that $Z = f circ X$, the composition of measurable maps, hence measurable.
That $X$ as defined above (the vector random variable) is measurable is clear. Let $B = B_1 times cdots B_n$, where $B_i in mathcal{B}(mathbf{R})$ for all $i$. Note that
$$
X^{-1}(B) = cap_{i=1}^n X_i^{-1}(B_i) in sigma(X_1, dots, X_n),
$$
where the membership follows since for each $i$, $X_i^{-1}(B_i) in sigma(X_i) subset sigma(X_1, dots X_n)$, and latter is closed under intersection. But then now use that the Borels on $mathbf{R}^n$ are generated by these `rectangular' sets, so hence $X$ is measurable. Is this roughly the idea?
probability-theory measure-theory
probability-theory measure-theory
asked Dec 8 '18 at 1:25
Drew BradyDrew Brady
682315
682315
$begingroup$
Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27
add a comment |
$begingroup$
Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27
$begingroup$
Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27
$begingroup$
Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27
add a comment |
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Yes. Your argument is correct.
$endgroup$
– Kavi Rama Murthy
Dec 8 '18 at 5:27