Multivariate Normal Distribution

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SUMMARY

The discussion focuses on the application of the multivariate normal distribution, specifically the formula for a three-dimensional case where the mean vector is zero and the covariance matrix is denoted as Σ. The key equation presented is f(x_1, x_2, x_3) = (1/(2π)^(3/2)|Σ|^(1/2))exp(-1/2 * x Σ^(-1) x). A crucial property highlighted is that any linear combination of a multivariate normal vector results in a univariate normal distribution, with defined expectations and variances based on the linear transformation.

PREREQUISITES
  • Understanding of multivariate normal distribution properties
  • Familiarity with covariance matrices and their determinants
  • Knowledge of linear algebra, specifically linear combinations
  • Basic statistics, including mean and variance concepts
NEXT STEPS
  • Study the derivation and applications of the multivariate normal distribution
  • Learn about covariance matrix properties in multivariate statistics
  • Explore linear transformations and their effects on distributions
  • Investigate practical applications of multivariate normal distribution in statistical modeling
USEFUL FOR

Students in statistics, data scientists, and anyone involved in statistical modeling or analysis requiring an understanding of multivariate distributions.

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Homework Statement



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Homework Equations





The Attempt at a Solution



I know that [tex]f(x_1, x_2, x_3) = \frac{1}{(2 \pi)^{3/2}|\Sigma|^{1/2}}exp(-\frac{1}{2}x \Sigma^{-1} x)[/tex] since n = 3 and mu = 0.

I've never used the multivariate normal distribution. My prof just derived it, but never taught us how to use it.

so does X1~N(mu,sigma11)?
 
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Yes, although you don't necessarily need it. Here is the useful property you will need for this problem.

Let [tex]\bold X[/tex] be multivariate normal with [tex]\bold \mu=E(\bold X)[/tex] and [tex]\bold \Sigma=Var(\bold X)[/tex].

Then any linear combination [tex]\bold a^T\bold X[/tex] is univariate normal with [tex]E(\bold a^T\bold X)=\bold a^T E(\bold X)[/tex] and [tex]Var(\bold a^T\bold X)=\bold a^T Var(\bold X) \bold a[/tex].
 

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