# Introduction to turbulence/Statistical analysis/Multivariate random variables

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 Revision as of 18:21, 1 June 2006 (view source)Michail (Talk | contribs) (→Joint pdfs and joint moments)← Older edit Revision as of 18:35, 1 June 2006 (view source)Michail (Talk | contribs) (→Joint pdfs and joint moments)Newer edit → Line 3: Line 3: Often it is importamt to consider more than one random variable at a time. For example, in turbulence the three components of the velocity vector are interralated and must be considered together. In addition to the ''marginal'' (or single variable) statistical moments already considered, it is necessary to consider the '''joint''' statistical moments. Often it is importamt to consider more than one random variable at a time. For example, in turbulence the three components of the velocity vector are interralated and must be considered together. In addition to the ''marginal'' (or single variable) statistical moments already considered, it is necessary to consider the '''joint''' statistical moments. - For example if $u$ and $v$ are two random variables, there are three second-order moments which can be defined $\left\langle u^{2} \right\rangle$ , $\left\langle v^{2} \right\rangle$ , and $\left\langle uv \right\rangle$. The product moment $\left\langle uv \right\rangle$ is called the ''cross-correlation'' or  ''cross-covariance''. The moments + For example if $u$ and $v$ are two random variables, there are three second-order moments which can be defined $\left\langle u^{2} \right\rangle$ , $\left\langle v^{2} \right\rangle$ , and $\left\langle uv \right\rangle$. The product moment $\left\langle uv \right\rangle$ is called the ''cross-correlation'' or  ''cross-covariance''. The moments $\left\langle u^{2} \right\rangle$ and $\left\langle v^{2} \right\rangle$ are referred to as the ''covariances'', or just simply the ''variances''. Sometimes $\left\langle uv \right\rangle$ is also referred to as the ''correlation''. + + In a manner similar to that used to build-up the probabilility density function from its measurable counterpart, the histogram, a '''joint probability density function''' (or '''jpdf'''),$B_{uv}$ , can be built-up from the ''joint histogram''. Figure 2.5 illustrates several examples of jpdf's which have different cross correlations. === The bi-variate normal (or Gaussian) distribution === === The bi-variate normal (or Gaussian) distribution === dssd dssd

## Revision as of 18:35, 1 June 2006

### Joint pdfs and joint moments

Often it is importamt to consider more than one random variable at a time. For example, in turbulence the three components of the velocity vector are interralated and must be considered together. In addition to the marginal (or single variable) statistical moments already considered, it is necessary to consider the joint statistical moments.

For example if $u$ and $v$ are two random variables, there are three second-order moments which can be defined $\left\langle u^{2} \right\rangle$ , $\left\langle v^{2} \right\rangle$ , and $\left\langle uv \right\rangle$. The product moment $\left\langle uv \right\rangle$ is called the cross-correlation or cross-covariance. The moments $\left\langle u^{2} \right\rangle$ and $\left\langle v^{2} \right\rangle$ are referred to as the covariances, or just simply the variances. Sometimes $\left\langle uv \right\rangle$ is also referred to as the correlation.

In a manner similar to that used to build-up the probabilility density function from its measurable counterpart, the histogram, a joint probability density function (or jpdf),$B_{uv}$ , can be built-up from the joint histogram. Figure 2.5 illustrates several examples of jpdf's which have different cross correlations.

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