Getting the joint probability density for the characteristic equation

In summary, the problem is about finding the characteristic function for the random variable Z = X2+Y2, where X and Y are independent and Gaussian distributed with first moment <x> = <y> = 0 and standard deviation σx = σy = 1. The solution involves finding the joint probability density Pz(z) and simplifying it using the characteristic equation and Gaussian distribution. The final result is ∫0∞ r δ(z - r2) dr = 1/2 for z > 0.
  • #1
schrodingerscat11
89
1
Dear all,

Greetings! I was given a problem from Reichl's Statistical Physics book. Thank you very much for taking time to read my post.

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]

[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{-1}{2}(z-x^2)} \, dy \, δ (z - x^2 +y^2) [/itex]

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

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

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

Question: How do I simplify this factor [itex] \int_{-\infty}^{+\infty} dx \, \int_{-\infty}^{+\infty}\frac{1}{\sqrt{2∏} } dy \, δ (z - x^2 +y^2) [/itex] ?

Thank you very much for your help! :biggrin:
 
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  • #2
hi physicsjn! :smile:
physicsjn said:
Question: How do I simplify this factor [itex] \int_{-\infty}^{+\infty} dx \, \int_{-\infty}^{+\infty}\frac{1}{\sqrt{2π} } dy \, δ (z - x^2 +y^2) [/itex] ?

change to ∫∫ rdrdθ, and then it's just δ(z - r2) :wink:
 
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  • #3
Are the integration limits correct?

Thank you tiny-tim! :biggrin: So I guess this will become
[itex]\int^{+\infty}_{-\infty}\int^{+\infty}_{-\infty}dx \,dy \, \delta(z-x^2-y^2) = \int^{+\infty}_{-\infty}\int^{2\pi}_{0} r \, dr \, d\theta \, \delta(z-r^2)[/itex]

[itex] = 2\pi\int^{+\infty}_{-\infty} r \, dr \, \, \delta(z-r^2)[/itex]

[itex] = 2\pi \, \frac{r^2}{2} \, \delta(z-r^2)\,|^{+\infty}_{-\infty} [/itex]

[itex] = \pi \, r^2 \, \delta(z-r^2)\,|^{+\infty}_{-\infty} [/itex]

Now, because of the delta term, all other r will be killed except when r2=z,

leaving us with

[itex]\int^{+\infty}_{-\infty}\int^{+\infty}_{-\infty}dx \,dy \, \delta(z-x^2-y^2) = \pi \, z [/itex]

Is this correct? :shy: Thanks again.. :)
 
  • #4
hi physicsjn! :smile:

(just got up :zzz:)

i] ##\int^{+\infty}_{-\infty}\int^{2\pi}_{0}## covers the plane twice, doesn't it? :wink:

ii]
physicsjn said:
[itex] = 2\pi\int^{+\infty}_{-\infty} r \, dr \, \, \delta(z-r^2)[/itex]

[itex] = 2\pi \, \frac{r^2}{2} \, \delta(z-r^2)\,|^{+\infty}_{-\infty} [/itex]

i don't follow what you're doing here :confused:

i'd say (for z > 0) ∫0 r δ(z - r2) dr

= ∫0 1/2 δ(z - u) du

= 1/2

(not sure that works for z = 0 :confused:)
 
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  • #5
Thank you tiny-tim! :biggrin:

i]
Ahhh... I see... My limits should be
[itex]\int^{+\infty}_{0} \int^{2\pi}_{0}[/itex]

And solving with this new limits should yield
[itex]\int^{+\infty}_{-\infty} \int^{+\infty}_{-\infty} dx \, dy \, δ(z-x^2-y^2)=\int^{+\infty}_{0} \int^{2\pi}_{0}r \, dr \, d\theta \, δ(z-r^2)[/itex]

[itex]=2\pi\int^{+\infty}_{0} r \, dr \,δ(z-r^2)[/itex]

ii]
I'm really sorry. The second line [itex]=2\pi\frac{r^2}{2}\delta(z-r^2) |_{-\infty}^{+\infty}
[/itex]is wrong. Just forget that I have written it. :redface:

But here's what I'm trying to say:
We want to evaluate [itex]2\pi\int^{+\infty}_{0} r \, dr \,δ(z-r^2)[/itex]
In my mind, I picture this integral as a sum of all the possible r values from 0 to infinity. However, the delta factor is zero everywhere except when [itex]r^2=z[/itex] or when [itex]r=\sqrt{z}[/itex]. In this case the delta function is just equal to one.
Hence, despite the integral being an infinite sum of r's, only one term will survive:[itex]r=\sqrt{z}[/itex]. The rest of the terms are just zeroes because of delta function.
So I thought [itex] \int^{+\infty}_{0} r \, dr \,δ(z-r^2)=\sqrt{z} [/itex]
It's similar to the what I remember as Fourier's trick. But I guess using your relation below makes things simpler, so just forget this Fourier's trick stuff. :smile:

[iii]
Wow! I totally don't know this. :bugeye: Thank you very much for this! :biggrin: I tried to show this in my to-be-submitted solution. I let u=r2 and solved for du and found [itex]\frac{du}{2}=r \, dr[/itex]. I can't see though why it shouldn't work for z=0. :confused: I'll just assume it'll work. :wink:

I think from here the rest is just plug and chug. Thank you so much tiny-tim for helping me! :biggrin: Sorry if I'm a bit slow. :redface:
 

Related to Getting the joint probability density for the characteristic equation

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

The joint probability density for the characteristic equation allows us to calculate the probability of multiple random variables occurring simultaneously. It is a fundamental tool in probability theory and is used in various applications such as statistical analysis and machine learning.

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 of each random variable together. This is based on the assumption that the random variables are independent of each other.

3. Can the joint probability density for the characteristic equation be used for dependent variables?

No, the joint probability density for the characteristic equation is only applicable to independent random variables. If the variables are dependent, a different approach, such as conditional probability, must be used to calculate the joint probability.

4. 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. Marginal probability density can be obtained from the joint probability density by summing or integrating over all possible values of the other variables.

5. How is the joint probability density for the characteristic equation used in practical applications?

The joint probability density for the characteristic equation is used in various fields such as physics, economics, and engineering to model and analyze complex systems. It is also used in machine learning algorithms to predict outcomes based on multiple variables. Additionally, it is used in risk assessment and decision making in fields such as finance and insurance.

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