Joint density functions (gaussian)

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SUMMARY

The joint probability density function of two independent standard Gaussian random variables X and Y, both distributed as N(0, 1), is given by the product of their individual densities. Specifically, the joint density function fXX,Y (x, y) can be expressed as fX(x) * fY(y), where fX(x) and fY(y) are the individual Gaussian density functions. Since both variables are independent, the multiplication of their densities is valid, confirming that the joint density function is indeed the product of the two Gaussian distributions.

PREREQUISITES
  • Understanding of Gaussian distributions, specifically N(0, 1)
  • Knowledge of joint probability density functions
  • Familiarity with the concept of independence in probability
  • Basic skills in probability theory and statistics
NEXT STEPS
  • Study the properties of joint probability distributions
  • Learn about the implications of independence in probability theory
  • Explore the derivation of the Gaussian density function
  • Investigate applications of joint density functions in statistical modeling
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Students and professionals in statistics, data science, and machine learning who are working with Gaussian distributions and joint probability concepts.

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Would just like a hand with this question

If X and Y are independent standard Gaussian random variables (that is, independent N(0, 1) 's ) do the following:
(a) Write down the joint probability density function fXX,Y (x, y) of X and Y .

I know what the gaussian density function looks like. Is it just a matter of multiplying two gaussian distributions together... where u have a σ1 and σ2 (do the same with the mean)
 
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Any time X, Y are independent continuous random variables their joint density is the product of their individual densities - so yes, all you need to do is multiply the individual densities.
 

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