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

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In summary, the conversation discusses the integration of a bivariate p.d.f. and the difficulty in finding the necessary terms. The formula includes quadratic terms in x and y and unknown terms such as $\sigma_x$, $\sigma_y$, and $\rho$. The conversation also mentions solving simultaneous equations to find the unknown terms.
  • #1
nacho-man
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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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  • #2
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

$\chi$ $\sigma$
 
  • #3
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
 

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

1.

What is a "tricky" joint PDF?

A tricky joint PDF (probability density function) refers to a mathematical function that describes the probability distribution of two or more random variables. It is considered "tricky" when it involves complex or non-standard distributions, making it challenging to calculate or interpret.

2.

How is a tricky joint PDF different from a regular joint PDF?

A tricky joint PDF differs from a regular joint PDF in that it involves more complex or non-standard distributions, making it more difficult to analyze and understand. Regular joint PDFs typically involve simpler distributions such as the normal or uniform distributions.

3.

What are some common techniques for dealing with tricky joint PDFs?

Some common techniques for dealing with tricky joint PDFs include using transformations, approximations, and simulation methods. Transformations involve converting the tricky joint PDF into a simpler form that is easier to work with. Approximations use simplified mathematical models to estimate the tricky joint PDF. Simulation methods involve generating random samples from the tricky joint PDF and using these samples to approximate the distribution.

4.

Why is it important to understand tricky joint PDFs?

Understanding tricky joint PDFs is important because they are often encountered in real-world problems and applications. For example, in fields such as finance, engineering, and physics, complex distributions may arise when modeling systems or processes. Understanding tricky joint PDFs allows researchers and scientists to accurately analyze and make predictions based on these distributions.

5.

What are some common examples of tricky joint PDFs?

Some common examples of tricky joint PDFs include the multivariate normal distribution, the beta distribution, and the gamma distribution. These distributions involve multiple variables and can be challenging to work with due to their non-standard forms. Other examples include the log-normal distribution, the Weibull distribution, and the Pareto distribution.

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