Beta distribution: trivial question

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The discussion focuses on whether it is possible to uniquely determine the parameters α and β of the beta distribution from its mean and variance. It highlights that this can be approached by solving a cubic equation derived from the relationships between the parameters and the statistical properties. The relationship between α and β is linear, leading to a maximum of three potential solutions. However, since both parameters must be greater than zero, this constraint can eliminate extraneous solutions. Ultimately, a unique method exists by transforming the mean formula to express β in terms of α, allowing the variance formula to yield linear relationships between the two parameters.
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We all know that it is easy to get the beta mean and variance given the parameters α and β of the distribution (http://en.wikipedia.org/wiki/Beta_distribution).

Can we do right the opposite? I.e. is there any way to go uniquely from mean and variance to the parameters of the beta?
 
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Based on the formulas in the link, the question can be reduced to solving a cubic equation in α or β as a function of the mean and variance. α and β are linear with respect to each other. Therefore there are at most three possible solutions. Since α and β are both required to be > 0, this could eliminate the extra solutions.
 
Yes, there's a unique way. Convert the formula for mean into one for \frac{\beta}{\alpha}, then use that formula to simplify the variance formula into something linear in \alpha and \beta. Then you'll have two straight-line relationships (one positively sloped, one negatively sloped) between the two parameters.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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