Probability density function

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Revision as of 11:31, 18 October 2005

Stochastic methods use distribution functions to decribe the fluctuacting scalars in a turbulent field.

The distribution function $F_\phi(\Phi)$ of a scalar $\phi$ is the probability $p$ of finding a value of $\phi < \Phi$

$F_\phi(\Phi) = p(\phi < \Phi)$

The probability of finding $\phi$ in a range $\Phi_1,\Phi_2$ is

$p(\Phi_1 <\phi < \Phi_2) = F_\phi(\Phi_2)-F_\phi(\Phi_1)$

The probability density function (PDF) is

$P(\Phi)= \frac{d F_\phi(\Phi)} {d \Phi}$

where $P(\Phi) d\Phi$ is the probability of $\phi$ being in the range $(\Phi,\Phi+d\Phi)$ . It follows that

$\int P(\Phi) d \Phi = 1$

Integrating over all the possible values of $\phi$ . The PDF of any stochastic variable depends "a-priori" on space and time.

$P(\Phi;x,t)$

From the PDF of a variable, one can define its $n$ th moment as

$\overline{\phi}^n = \int \phi^n P(\Phi) d \Phi$

the $n = 1$ case is called the "mean".

$\overline{\phi} = \int \phi P(\Phi) d \Phi$

Similarly the mean of a function can be obtained as

$\overline{f} = \int f(\phi) P(\Phi) d \Phi$

Where the second central moment is called the "variance"

$\overline{u'^2} = \int (\phi-\overline{\phi}) P(\Phi) d \Phi$

For two variables (or more) a joint-PDF of $\phi$ and $\psi$ is defined

$P(\Phi,\Psi;x,t)$

and the marginal PDF's are obatined by integration over the sample space of one variable.

$P(\Phi) = \int P(\Phi,\Psi) d\Psi$

For two variables the correlation is given by

$\overline{\phi' \psi'} = \int (\phi-\overline{\phi}) (\psi-\overline{\psi}) P(\Phi,\Psi) d \Phi d\Psi$

This term often appears in turbulent flows the averaged Navier-Stokes (with $u, v$ ) and is unclosed.

Using Bayes' theorem a joint-pdf can be expressed as

$P(\Phi,\Psi) = P(\Phi|\Psi) P(\Psi)$

where $P(\Phi|\Psi)$ is the conditional PDF.

If two variables are uncorrelated then they are statistically independent and their joint PDF can be expressed as a product of their marginal PDFs.

$P(\Phi,\Psi)= P(\Phi) P(\Psi)$

Probability density function

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@@ Line 29: / Line 29: @@
 The PDF of any stochastic variable depends "a-priori" on space and time.
 :<math> P(\Phi;x,t) </math>
+From the PDF of a variable, one can define its <math> n </math>th moment as
+:<math>
+\overline{\phi}^n = \int \phi^n  P(\Phi) d \Phi
+</math>
+the  <math> n = 1 </math> case is called the "mean".
+:<math>
+\overline{\phi} =  \int \phi P(\Phi) d \Phi
+</math>
+Similarly the mean of a function can be obtained as
+:<math>
+\overline{f} = \int f(\phi) P(\Phi) d \Phi
+</math>
+Where the second central moment is called the "variance"
+:<math>
+\overline{u'^2} = \int (\phi-\overline{\phi}) P(\Phi) d \Phi
+</math>
+For two variables (or more) a joint-PDF  of <math> \phi </math> and <math> \psi </math> is defined
+:<math> P(\Phi,\Psi;x,t) </math>
+and the marginal PDF's are obatined by integration over the sample space of one variable.
+:<math>
+P(\Phi) = \int P(\Phi,\Psi) d\Psi
+</math>
+For two variables the correlation is given by
+:<math>
+\overline{\phi' \psi'} = \int (\phi-\overline{\phi}) (\psi-\overline{\psi}) P(\Phi,\Psi) d \Phi d\Psi
+</math>
+This term often appears in turbulent flows the averaged Navier-Stokes (with <math> u, v </math>) and is unclosed.
+Using Bayes' theorem a joint-pdf can be expressed as
+:<math>
+P(\Phi,\Psi) = P(\Phi|\Psi) P(\Psi)
+</math>
+where  <math> P(\Phi|\Psi) </math> is the conditional PDF.
+If two variables are uncorrelated then they are statistically independent and their joint PDF can be expressed as a product of their marginal PDFs.
+:<math>
+P(\Phi,\Psi)= P(\Phi) P(\Psi)
+</math>