Multiplying Normal Distributions: Rules & Examples

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chota
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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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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*}<br /> S & \sim n(\mu_S, \sigma^2_S)\\<br /> D & \sim n(\mu_D, \sigma^2_D)<br /> \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.
 
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.
 
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