Relation Between Gaussians

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  • #1
thatboi
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Hi all,
Suppose I had some some n-dimensional vectors ##\vec{a}_{1}, \vec{a}_{2}, \vec{b}_{1},\vec{b}_{2}## that satisfied ##e^{||\vec{a}_{1}||^2}+e^{||\vec{a}_{2}||^2}=e^{||\vec{b}_{1}||^2}+e^{||\vec{b}_{2}||^2}##. Now suppose there was another non-zero n-dimensional vector ##\vec{A}##. Is there anything I can say about the equation ##e^{||\vec{a}_{1}-\vec{A}||^2}+e^{||\vec{a}_{2}-\vec{A}||^2}=e^{||\vec{b}_{1}-\vec{A}||^2}+e^{||\vec{b}_{2}-\vec{A}||^2}##? For example, is the equation satisfied for ##\vec{a}_{i} \neq \vec{b}_{j}## for ##i,j = {1,2}##. Also I mean ##||\cdot||## as in the L2 norm.
 
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Can you find an example that fails your equation? Start with a simple (1-dimensional) case.
 
  • #3
scottdave said:
Can you find an example that fails your equation? Start with a simple (1-dimensional) case.
I suppose even in the 1-dimensional case, my second equation is satisfied as long as ##a_{1}=b_{1}##, ##a_{2}=b_{2}## or vice versa right. I was just wondering if there was some other solutions ##a_{1},b_{1}## that satisfied the set of equations above.
 
  • #4
thatboi said:
I suppose even in the 1-dimensional case, my second equation is satisfied as long as ##a_{1}=b_{1}##, ##a_{2}=b_{2}## or vice versa right. I was just wondering if there was some other solutions ##a_{1},b_{1}## that satisfied the set of equations above.
What happens when ##a_{1}= -b_{1}##, ##a_{2}= -b_{2}## for example?
 

1. What is the relation between two Gaussians?

When we say there is a relation between two Gaussians, we are typically referring to their sum or product. The sum of two independent Gaussian random variables is also a Gaussian random variable, and the product of two Gaussian random variables is not Gaussian but follows a different distribution.

2. How can we calculate the sum of two Gaussian distributions?

To calculate the sum of two Gaussian distributions, you simply add the means of the two distributions to get the mean of the resulting distribution. The variance of the resulting distribution is the sum of the variances of the two input distributions.

3. What happens when we multiply two Gaussian distributions?

When we multiply two Gaussian distributions, the resulting distribution is not Gaussian but follows a different distribution called the product of two Gaussian distributions. This distribution is not as straightforward to calculate as the sum of two Gaussians.

4. Can you provide an example of the relation between Gaussians?

Sure! Let's say we have two Gaussian random variables X and Y with means μ1 and μ2, and variances σ1^2 and σ2^2, respectively. The sum of X and Y, X + Y, is also Gaussian with mean μ1 + μ2 and variance σ1^2 + σ2^2.

5. Why is understanding the relation between Gaussians important in statistics?

Understanding the relation between Gaussians is crucial in statistics because Gaussian distributions are widely used to model natural phenomena and measurement errors. Knowing how Gaussian distributions interact when combined or multiplied helps in making accurate predictions and inferences in various statistical analyses.

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