MHB Unlock the Mystery of Tricky Joint PDF with Expert Tips | Attached Image

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The discussion revolves around the challenges of integrating a complex bivariate probability density function (PDF) as depicted in the attached image. Participants express frustration over the difficulty of identifying necessary parameters, particularly the unknown terms σx, σy, and ρ. The equations provided suggest relationships between these parameters, but one user notes the challenge of solving them simultaneously. Another user reports progress by establishing a relationship between σx and σy but admits to not fully resolving the equations. The conversation highlights the complexities involved in working with statistical distributions and the need for clearer integration strategies.
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Please refer to the attached image.

How am I supposed to integrate this, it's impossible to find anything!
 

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nacho said:
Please refer to the attached image.

How am I supposed to integrate this, it's impossible to find anything!

Also in this case try to avoid a 'brute force approach' supposing the f(x,y) is a normal bivariate p.d.f. like...

$\displaystyle f(x,y) = \frac{1}{2\ \pi\ \sigma_{x}\ \sigma_{y}\ \sqrt{1- \rho^{2}}}\ e^{- \frac{z}{2\ (1-\rho^{2})}}\ (1)$

... where...

$\displaystyle z = \frac{(x-\mu_{x})^{2}}{\sigma_{x}^{2}} + \frac{(y-\mu_{y})^{2}}{\sigma_{y}^{2}} - 2\ \frac{\rho\ (x - \mu_{x})\ (y-\mu_{y})}{\sigma_{x}\ \sigma_{y}}\ (2)$

In Your formula there are in the exponent only quaqdratic terms in x and y, so that is $\displaystyle \mu_{x}=\mu_{y}=0$ and that means that You have to find the unknown terms $\sigma_{x}$, $\sigma_{y}$ and $\rho$. That can be performed writing...

$\displaystyle \sigma^{2}_{x}\ (1-\rho^{2}) = \frac{1}{4}$

$\displaystyle \sigma^{2}_{y}\ (1-\rho^{2})= \frac{1}{9}$

$\displaystyle \sigma_{x}\ \sigma_{y}\ \frac{1-\rho^{2}}{\rho} = \frac{1}{6}\ (3)$

Are you able to solve the (3) finding $\sigma_{x}$, $\sigma_{y}$ and $\rho$?...

Kind regards

$\chi$ $\sigma$
 
I can't see any way to make simultaneous equations here.

I ended up getting

$\sigma_x = \frac{3\sigma_y}{2} $ but that's as close as i could get
 
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