Stochastic processes with same distribution, which are no modification












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Consider stochastic processes $mathcal{X}$ and $mathcal{Y}$. How can I find an example in which $mathcal{X}$ and $mathcal{Y}$ having the same distribution but are no modification of each other? I think I understood the concept of modification. But how do I find the distributions for $mathcal{X}$ and $mathcal{Y}$? I know that there can be defined a distribution by kolmogorov's extension theorem, but I wonder how this should look like. Any hint or help will be appreciated. Thanks in advance!










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  • $begingroup$
    Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
    $endgroup$
    – herb steinberg
    Dec 4 '18 at 1:03










  • $begingroup$
    If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
    $endgroup$
    – saz
    Dec 4 '18 at 7:59


















0












$begingroup$


Consider stochastic processes $mathcal{X}$ and $mathcal{Y}$. How can I find an example in which $mathcal{X}$ and $mathcal{Y}$ having the same distribution but are no modification of each other? I think I understood the concept of modification. But how do I find the distributions for $mathcal{X}$ and $mathcal{Y}$? I know that there can be defined a distribution by kolmogorov's extension theorem, but I wonder how this should look like. Any hint or help will be appreciated. Thanks in advance!










share|cite|improve this question









$endgroup$












  • $begingroup$
    Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
    $endgroup$
    – herb steinberg
    Dec 4 '18 at 1:03










  • $begingroup$
    If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
    $endgroup$
    – saz
    Dec 4 '18 at 7:59
















0












0








0





$begingroup$


Consider stochastic processes $mathcal{X}$ and $mathcal{Y}$. How can I find an example in which $mathcal{X}$ and $mathcal{Y}$ having the same distribution but are no modification of each other? I think I understood the concept of modification. But how do I find the distributions for $mathcal{X}$ and $mathcal{Y}$? I know that there can be defined a distribution by kolmogorov's extension theorem, but I wonder how this should look like. Any hint or help will be appreciated. Thanks in advance!










share|cite|improve this question









$endgroup$




Consider stochastic processes $mathcal{X}$ and $mathcal{Y}$. How can I find an example in which $mathcal{X}$ and $mathcal{Y}$ having the same distribution but are no modification of each other? I think I understood the concept of modification. But how do I find the distributions for $mathcal{X}$ and $mathcal{Y}$? I know that there can be defined a distribution by kolmogorov's extension theorem, but I wonder how this should look like. Any hint or help will be appreciated. Thanks in advance!







probability probability-theory stochastic-processes






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share|cite|improve this question











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asked Dec 4 '18 at 0:14









user408858user408858

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490111












  • $begingroup$
    Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
    $endgroup$
    – herb steinberg
    Dec 4 '18 at 1:03










  • $begingroup$
    If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
    $endgroup$
    – saz
    Dec 4 '18 at 7:59




















  • $begingroup$
    Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
    $endgroup$
    – herb steinberg
    Dec 4 '18 at 1:03










  • $begingroup$
    If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
    $endgroup$
    – saz
    Dec 4 '18 at 7:59


















$begingroup$
Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
$endgroup$
– herb steinberg
Dec 4 '18 at 1:03




$begingroup$
Let X be a stationary Gaussian process with a given power spectrum. Let Y be another stationary Gaussian process with the same power spectrum. They meet your requirement. Am I missing something?
$endgroup$
– herb steinberg
Dec 4 '18 at 1:03












$begingroup$
If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
$endgroup$
– saz
Dec 4 '18 at 7:59






$begingroup$
If $(B_t)_t$ is a Brownian motion, you can consider $X_t = B_t$ and $Y_t = - B_t$.
$endgroup$
– saz
Dec 4 '18 at 7:59












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