Getting the joint probability density for the characteristic equation

In summary, the problem involves finding the characteristic function and moments of a random variable Z, which is the sum of two independent and Gaussian distributed variables X and Y. The joint probability density and characteristic equation are used to solve for the characteristic function, and moments and cumulants are computed based on the Gaussian distribution.
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schrodingerscat11
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Homework Statement



The stochastic variables X and Y are independent and Gaussian distributed with
first moment <x> = <y> = 0 and standard deviation σx = σy = 1. Find the characteristic function
for the random variable Z = X2+Y2, and compute the moments <z>, <z2> and <z3>. Find the first 3 cumulants.

Homework Equations


Characteristic equation: [itex]f_z (k) = <e^{ikz}> = \int_{-\infty}^{+\infty} e^{ikz}\, P_z (z) dz[/itex]

Joint Probability density: [itex] P_z(z) = \int_{-\infty}^{+\infty} dx \, \int_{-\infty}^{+\infty} dy \, δ (z - G(x,y)) P_{x,y}(x,y) [/itex] where [itex] z = G (x, y) [/itex]

Also, [itex]P_{x,y} = P_x (x) \, P_y (y) [/itex] for independent stochastic variables x and y.

For Gaussian distribution: [itex] P_x = \frac{1}{\sqrt{2∏} } e^{\frac{-x^2}{2}} [/itex]

The Attempt at a Solution


To get the characteristic equation, we need first to get the joint probability density Pz(z):

Since [itex] G(x,y)= x^2 +y^2 [/itex] and [itex]P_{x,y} = P_x (x) \, P_y (y) [/itex]

[itex] P_z(z) = \int_{-\infty}^{+\infty} dx \, \int_{-\infty}^{+\infty} dy \, δ (z - x^2 +y^2) P_x (x) P_y (y) [/itex]

[itex] P_z(z) = \int_{-\infty}^{+\infty}P_x (x) \, dx \, \int_{-\infty}^{+\infty}P_y (y) \, dy \, δ (z - x^2 +y^2) [/itex]

[itex] P_z(z) = \int_{-\infty}^{+\infty}\frac{1}{\sqrt{2∏} } e^{\frac{-x^2}{2}} \, dx \, \int_{-\infty}^{+\infty}\frac{1}{\sqrt{2∏} } e^{\frac{-y^2}{2}} \, dy \, δ (z - x^2 +y^2) [/itex]











 
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Related to Getting the joint probability density for the characteristic equation

1. What is the joint probability density for the characteristic equation?

The joint probability density for the characteristic equation is a mathematical function that describes the probability of multiple variables occurring simultaneously. It is used to model the relationships between different variables in a system and can be used to predict the likelihood of certain outcomes.

2. How is the joint probability density for the characteristic equation calculated?

The joint probability density for the characteristic equation is calculated by multiplying the individual probability density functions for each variable in the equation. This can be represented as a multidimensional integral, where each variable is integrated over its range of possible values.

3. What is the difference between joint probability density and marginal probability density?

Joint probability density refers to the probability of multiple variables occurring simultaneously, while marginal probability density refers to the probability of a single variable occurring on its own. In other words, joint probability density takes into account the relationships between variables, while marginal probability density focuses on a single variable.

4. Why is it important to calculate the joint probability density for the characteristic equation?

Calculating the joint probability density for the characteristic equation is important because it allows us to understand the relationships between variables in a system. This can be useful for making predictions and understanding the behavior of complex systems.

5. Can the joint probability density for the characteristic equation be used for both continuous and discrete variables?

Yes, the joint probability density for the characteristic equation can be used for both continuous and discrete variables. For continuous variables, it is represented as a continuous function, while for discrete variables, it is represented as a discrete function (such as a probability mass function).

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