Upper bound for independent Normal random variables The Next CEO of Stack OverflowProbability involving dependent normal variablesMaximum of a sequence of almost-identical independent normal random variablesUpper bound for the gaussian measure of an epsilon strip.Upper bound on the entropy of a sum two random variablesDifferential entropy of the product of Gaussian random variablesConvergence of normal distributed random variablesIf $X$ and $Y$ are two NON independent random normal variables, what is the distribution of $Z = fracXY^n$Sum of independent normal random variablesapproximation of product of two independent Normal random variablesAsymptotics of expected value of maximum of normal variables
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Upper bound for independent Normal random variables
The Next CEO of Stack OverflowProbability involving dependent normal variablesMaximum of a sequence of almost-identical independent normal random variablesUpper bound for the gaussian measure of an epsilon strip.Upper bound on the entropy of a sum two random variablesDifferential entropy of the product of Gaussian random variablesConvergence of normal distributed random variablesIf $X$ and $Y$ are two NON independent random normal variables, what is the distribution of $Z = fracXY^n$Sum of independent normal random variablesapproximation of product of two independent Normal random variablesAsymptotics of expected value of maximum of normal variables
$begingroup$
I want to fined a condition for variance of independent normal random variables to show that Entropy Integral is finite.
I guess i have to show, if sigma_n decreases to 0 then is the Entropy Integral
finite but i don't know how?
Let $X_nsim N(0,sigma_n)$ be independent. I want to show , is there a condition for $sigma_n$ that shows Dudley Integral is right. Because if Dudley Integral(Entropy Integral) is finite then we can say our variables has Upper bound.
$X_n sim N( 0, sigma_n)$
is $int _0^d sqrtlog N(epsilon)< infty $?
normal-distribution entropy upper-lower-bounds
asked yesterday
TaraTara
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New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
add a comment |
$begingroup$
I want to fined a condition for variance of independent normal random variables to show that Entropy Integral is finite.
I guess i have to show, if sigma_n decreases to 0 then is the Entropy Integral
finite but i don't know how?
Let $X_nsim N(0,sigma_n)$ be independent. I want to show , is there a condition for $sigma_n$ that shows Dudley Integral is right. Because if Dudley Integral(Entropy Integral) is finite then we can say our variables has Upper bound.
$X_n sim N( 0, sigma_n)$
is $int _0^d sqrtlog N(epsilon)< infty $?
normal-distribution entropy upper-lower-bounds
asked yesterday
TaraTara
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday
add a comment |
$begingroup$
I want to fined a condition for variance of independent normal random variables to show that Entropy Integral is finite.
I guess i have to show, if sigma_n decreases to 0 then is the Entropy Integral
finite but i don't know how?
Let $X_nsim N(0,sigma_n)$ be independent. I want to show , is there a condition for $sigma_n$ that shows Dudley Integral is right. Because if Dudley Integral(Entropy Integral) is finite then we can say our variables has Upper bound.
$X_n sim N( 0, sigma_n)$
is $int _0^d sqrtlog N(epsilon)< infty $?
normal-distribution entropy upper-lower-bounds
asked yesterday
TaraTara
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I want to fined a condition for variance of independent normal random variables to show that Entropy Integral is finite.
I guess i have to show, if sigma_n decreases to 0 then is the Entropy Integral
finite but i don't know how?
Let $X_nsim N(0,sigma_n)$ be independent. I want to show , is there a condition for $sigma_n$ that shows Dudley Integral is right. Because if Dudley Integral(Entropy Integral) is finite then we can say our variables has Upper bound.
$X_n sim N( 0, sigma_n)$
is $int _0^d sqrtlog N(epsilon)< infty $?
normal-distribution entropy upper-lower-bounds
normal-distribution entropy upper-lower-bounds
asked yesterday
TaraTara
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
TaraTara
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Tara
asked yesterday
TaraTara
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
TaraTara
11
asked yesterday
TaraTara
11
11
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Tara is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday
add a comment |
$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday
$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday
add a comment |
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$begingroup$
I see problems with the square root of a negative number due to the log. Are you sure about the expression you need to integrate? Assuming that you can get to a correct expression, in order to prove something like you said, my suggestion would be to make the transformation to the standardized normal distribution and then show that the transformed value of $d$, although increasing keeps the computation of the integral bounded.
$endgroup$
– Ertxiem
yesterday
$begingroup$
Thank you for your Comment. Actually i fined for a condition for variance of independent Normal variables to show that these variables has Upper bound with Entropy Integral.
$endgroup$
– Tara
yesterday
$begingroup$
I took a look at wikipedia and I'm not sure that the $N$ that appears there is the Normal distribution. If I remember correctly what I learned from Statistical Physics $N$ may be the number of states or something close to it.
$endgroup$
– Ertxiem
yesterday