LaPlacian joint probability density function.

In summary: What is the best way to find the boundaries? You can use the Jacobian to find the boundaries, but be sure to take into account the absolute value constraints.
  • #1
marina87
22
0
A joint pdf is given as pxy(x,y)=(1/4)^2 exp[-1/2 (|x| + |y|)] for x and y between minus and plus infinity.

Find the joint pdf W=XY and Z=Y/X.

f(w,z)=∫∫f(x,y)=∫∫(1/4)^2*e^(-(|x|+|y|)/2)dxdy -∞<x,y<∞
Someone told me I can not use Jacobian because of the absolute value. Is that true?
So far this is what I have but I feel like I am not going anywhere.

f(w,z)=(1/4)^2∫∫e^(-(|x|+|y|)/2)dxdy
=(1/4)^2∫∫[e^-|x|/2]*e^-|y|/2]
=(1/4)^2∫[e^-|x|/2]∫e^-|y|/2]

=(1/4)^2[∫[e^(-x/2)+∫e^(x/2))] * [∫[e^(-y/2)+∫e^(y/2))] the limits from -∞<x,y<0 and 0<x,y<∞

=[(1/4)^2 ]*4*[ ∫ [e^(x/2)dx] + ∫ [e^(y/2)dy] ] 0<x,y<∞
I have problems with transforming the limits for the new functions.
 
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  • #2
marina87 said:
A joint pdf is given as pxy(x,y)=(1/4)^2 exp[-1/2 (|x| + |y|)] for x and y between minus and plus infinity.

Find the joint pdf W=XY and Z=Y/X.

f(w,z)=∫∫f(x,y)=∫∫(1/4)^2*e^(-(|x|+|y|)/2)dxdy -∞<x,y<∞
Someone told me I can not use Jacobian because of the absolute value. Is that true?
So far this is what I have but I feel like I am not going anywhere.

f(w,z)=(1/4)^2∫∫e^(-(|x|+|y|)/2)dxdy
=(1/4)^2∫∫[e^-|x|/2]*e^-|y|/2]
=(1/4)^2∫[e^-|x|/2]∫e^-|y|/2]

=(1/4)^2[∫[e^(-x/2)+∫e^(x/2))] * [∫[e^(-y/2)+∫e^(y/2))] the limits from -∞<x,y<0 and 0<x,y<∞

=[(1/4)^2 ]*4*[ ∫ [e^(x/2)dx] + ∫ [e^(y/2)dy] ] 0<x,y<∞



I have problems with transforming the limits for the new functions.

Of course you can use the Jacobian, but you need to be careful to ensure that the variables therein do not cross zero; that is, |x| is perfectly well differentiable as long as x > 0 or x < 0. So, for positive w and z, what is the WY-region of {w < W < w+dw, z < Z < z+dz}? You ought to be able to find it as the union of two disjoint subregions, and in each subregion you can certainly use a Jacobian.

Now you need to look at the other cases where w> 0 and z > 0 do not both hold.
 
  • #3
That is the part where I got stuck.
What is the best way to solve this problem? should I use Jacobian or should I use the distribution properties?

But my biggest question and where I need help is with the boundaries.
 

Related to LaPlacian joint probability density function.

1. What is the LaPlacian joint probability density function?

The LaPlacian joint probability density function is a mathematical function that describes the probability distribution of two or more random variables. It is often used in statistics and probability to model the relationship between two or more variables.

2. How is the LaPlacian joint probability density function different from other probability density functions?

The LaPlacian joint probability density function differs from other probability density functions in that it takes into account the joint probability of multiple variables, rather than just the probability of one variable. This allows for a more comprehensive understanding of the relationship between variables.

3. What is the purpose of using the LaPlacian joint probability density function?

The LaPlacian joint probability density function is used for a variety of purposes, such as predicting outcomes, analyzing relationships between variables, and modeling complex systems. It is also commonly used in machine learning and data analysis.

4. How is the LaPlacian joint probability density function calculated?

The LaPlacian joint probability density function is calculated by taking the derivative of the joint probability distribution function. This requires knowledge of the joint probability function, which can be calculated using statistical methods or estimated from data.

5. What are some real-world applications of the LaPlacian joint probability density function?

The LaPlacian joint probability density function has many real-world applications, including risk assessment, stock market analysis, weather forecasting, and image processing. It is also used in fields such as engineering, physics, and economics to model complex systems and make predictions based on multiple variables.

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