Multiplying Normal Distributions: Rules & Examples

In summary: I think you are right that the question is about probabilities. In that case the answer would be something like "the convolution of two normal distributions is not a normal distribution. If the distributions are independent, the convolution will be the distribution of the sum. If the distributions are not independent, you will need to know the correlation between them."In summary, the question is about the general rule for multiplying two normal distributions and the answer is that when two normal random variables are independent, their sum will be a normal distribution with mean equal to the sum of the individual means and variance equal to the sum of the individual variances. However, if the two distributions are not independent, the convolution of the two will not be a normal distribution
  • #1
chota
22
0
Hi say I have two "independent" Normal distributions,

S ~ N(0,3^2) and D~(0,2^2)

since I know that S and D are indpendent then

P(S ) + P(D) = P(S)P(D)

however we know they are both normal distributed so I amm just wondering what the general rule is for multiplying two normal distributions
thanks
 
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  • #2
I'm not sure what you mean by

[tex]
P(S) + P(D) = P(S) P(D)
[/tex]

Are you trying to say that when normal random variables are added, the resulting random variable is their product? Not true.

If

[tex]
\begin{align*}
S & \sim n(\mu_S, \sigma^2_S)\\
D & \sim n(\mu_D, \sigma^2_D)
\end{align*}
[/tex]

and they are independent, then the sum [tex] S + D [/tex] is normal, with mean

[tex]
\mu_S + \mu_D
[/tex]

and variance

[tex]
\sigma^2_S + \sigma^2_D
[/tex]

A similar result is true even if the two variables have non-zero correlation (the formula for the variance of the sum involves the correlation).

If by 'product' [tex] P(S) P(D) [/tex] you mean the convolution of the distributions, you could go through that work, but it leads you to the same result I quoted above.
 
  • #3
chota said:
... since I know that S and D are indpendent then

P(S ) + P(D) = P(S)P(D)

I'm guessing you meant to say

P(S & D) = P(S)P(D)

where "S" here really means a statement along the lines of "S lies between A and B", and similarly for "D".
 
  • #4
For events A and B, normally distributed or not, P(A&B)= P(A)P(B|A)= P(B)P(B|A) where P(A|B) and P(B|A) are the "conditional probabilities" : P(A|B) is "the probability that A will happen given that B happened" and P(B|A) is "the probability that B will happen given that A happened".

IF the A and B are independent then P(A|B)= P(A) and P(B|A)= P(B) so you just multiply the separate probabilities. If they are not independent, just knowing the probabilities of each separately is not enough. You must know at least one of P(A|B), P(B|A) or P(A&B) separately from the individual probabilities.
 
  • #5
I answered as I did because

  • the OP used [tex] S, D[/tex] in his notation, and I took these as the names of the random variables rather than any interval or event.
  • I took the question to mean he was asking how to combine normal distributions rather than calculate any particular probability
 

FAQ: Multiplying Normal Distributions: Rules & Examples

What is the purpose of multiplying normal distributions?

The purpose of multiplying normal distributions is to combine two or more normal distributions in order to obtain a new distribution that reflects the combined effects of the individual distributions. This is useful in many real-world scenarios, such as predicting the performance of a system that is influenced by multiple factors.

What are the rules for multiplying normal distributions?

The rules for multiplying normal distributions are as follows:

  • The mean of the new distribution is equal to the sum of the means of the individual distributions.
  • The variance of the new distribution is equal to the sum of the variances of the individual distributions.
  • If the two distributions are independent, the standard deviation of the new distribution is equal to the square root of the sum of the squares of the individual standard deviations.
  • If the two distributions are not independent, the standard deviation of the new distribution is calculated using a more complex formula.

Can you provide an example of multiplying two normal distributions?

Yes, suppose we have two normal distributions with means 10 and 15, and standard deviations 2 and 3, respectively. By applying the rules for multiplying normal distributions, we can calculate the mean of the new distribution as 10 + 15 = 25 and the standard deviation as sqrt(2^2 + 3^2) = sqrt(13) ≈ 3.605. This new distribution represents the combined effects of the two individual distributions.

How does multiplying normal distributions affect the shape of the resulting distribution?

Multiplying normal distributions does not change the shape of the resulting distribution. The new distribution will still be bell-shaped and symmetric, as long as the individual distributions are also normal. However, the mean and standard deviation of the new distribution will be different from those of the individual distributions.

Are there any limitations or assumptions when multiplying normal distributions?

Yes, there are a few limitations and assumptions when multiplying normal distributions:

  • The individual distributions must be normal.
  • The distributions must be independent, or the more complex formula for calculating standard deviation must be used.
  • If the distributions are not independent, it is assumed that they have a linear relationship.
  • The rules for multiplying normal distributions may not be applicable in some cases, such as when multiplying more than two distributions or when the distributions are not normal.
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