Multivariate distribution : Mean vector?

In summary, the question asks about the concept of substitutes in relation to joint solutions in a probability density function and multivariate normal distributions. The asker also inquires about the theoretical basis and mathematical explanation for these concepts. The answer provides the mean vector for the universe and asks for theoretical and proof-based assistance.
  • #1
aslanbey42
2
0
Hello friends.My English is bad :) .I'll try to explain my trouble.

In question: Q function according to x1 and x2 are substitutes when the joint comes out of solution. Of the solution in theory I do not understand where they come from.Is there another solution or the problem? Where is the theoretical? Can you please explain mathematically?

----Question---

Probabilty density function:

[tex]f(x)=\frac{1}{\pi}\left(exp\left(\frac{-1}{2}\left(9x^{2}_{1}+2x^{2}_{2}+8x_{1}x_{2}-20x_{1}-8x_{2}+44\right)\right)\right)[/tex]

Multivariate normal distibitions

mean vector of the universe?

Answer:

[tex]\mu=\left(2,-2\right)[/tex]

--------------


Thanks
 
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  • #2
Please Help ME :D

Teory and proof...

Thanks...
 

FAQ: Multivariate distribution : Mean vector?

What is a multivariate distribution?

A multivariate distribution is a probability distribution that involves more than one variable. It describes the relationship between multiple variables and their probabilities of occurring together.

What is a mean vector in a multivariate distribution?

A mean vector in a multivariate distribution is a vector that contains the mean values for each variable in the distribution. It represents the center of the distribution and is calculated by taking the average of each variable's values.

How is the mean vector calculated in a multivariate distribution?

The mean vector in a multivariate distribution is calculated by taking the average of each variable's values. This can be done by adding up all the values for each variable and dividing by the total number of observations.

Why is the mean vector important in a multivariate distribution?

The mean vector is important in a multivariate distribution because it provides information about the central tendencies of the distribution. It can help to identify the most likely values for each variable and can be used to make predictions about future observations.

How does the mean vector relate to other measures of central tendency in a multivariate distribution?

The mean vector is one of several measures of central tendency in a multivariate distribution. It is related to other measures such as the median and mode, but it provides a more complete picture of the distribution as it takes into account the values of all variables.

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