I need your help in order to clarify a doubt...I have two samples:  sample $A$ with mean $\mu_A$ and standard deviation $\sigma_A$   sample $B$ with mean $\mu_B$ and standard deviation $\sigma_B$   I know the observed values from the sample $B$ but I don't know anything on sample $A$, is there a way to find mean and standard deviation of the combined sample $A \cup B$ ?  We can assume that the two samples are i.i.d within and between them, so I think I can compute:  $\mu_{A \cup B}$ = $\mu_A + \mu_B$  $\sigma_{A \cup B} = \sqrt{\sigma_A + \sigma_B}$  am I right? Thank you!!!
I'm calculating the covariance of a distribution in parallel and I need to combine the distributed results into on singular Gaussian. How do I combine the two?  Linearly interpolating between the two almost works, if they are similarly distributed and sized.   provides a forumla at the bottom for combination but it doesn't seem right; two identically distributed distributions should have the same covariance, but the formula at the bottom of the page doubles the covariance.  Is there a way to combine two matrices?
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