Getting the joint probability density for the characteristic equation

Click For Summary
SUMMARY

The discussion focuses on deriving the joint probability density function for the random variable Z = X² + Y², where X and Y are independent Gaussian variables with means of 0 and standard deviations of 1. The characteristic function is defined as f_z(k) = = ∫ e^{ikz} P_z(z) dz. The joint probability density P_z(z) is computed using the relationship P_z(z) = ∫∫ δ(z - G(x,y)) P_{x,y}(x,y) dx dy, where G(x,y) = x² + y² and P_{x,y} = P_x(x) P_y(y). The Gaussian distribution for P_x and P_y is given by P_x = (1/√(2π)) e^{(-x²/2)}.

PREREQUISITES
  • Understanding of Gaussian distributions and their properties
  • Familiarity with characteristic functions in probability theory
  • Knowledge of joint probability density functions
  • Basic calculus, specifically integration techniques
NEXT STEPS
  • Study the derivation of characteristic functions for multivariate distributions
  • Learn about the properties and applications of cumulants in statistics
  • Explore the use of Dirac delta functions in probability density calculations
  • Investigate the implications of independence in joint distributions
USEFUL FOR

Students and researchers in statistics, probability theory, and applied mathematics, particularly those working with Gaussian distributions and joint probability densities.

schrodingerscat11
Messages
86
Reaction score
1

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: f_z (k) = &lt;e^{ikz}&gt; = \int_{-\infty}^{+\infty} e^{ikz}\, P_z (z) dz

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

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

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

The Attempt at a Solution


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

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

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

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

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)











 
Physics news on Phys.org
I'm very sorry; I accidentally pressed the submit post button instead of preview post. How do I erase this? I'm not yet done with my post. :(
 

Similar threads

  • · Replies 2 ·
Replies
2
Views
2K
Replies
4
Views
2K
  • · Replies 2 ·
Replies
2
Views
1K
Replies
19
Views
3K
  • · Replies 19 ·
Replies
19
Views
3K
Replies
4
Views
2K
Replies
1
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K