It depends on how you want your linear model to affect the beta model. As you have mu
described here, it is not the mean of the beta, its just the normalizing constant of the mean. If you want alpha and beta described as the mean and variance of the beta, it is something like the following:
alpha = mu * (mu*(1-mu)/var - 1)
beta = (1 - mu) * (mu*(1-mu)/var - 1)
Maybe a simpler approach would be in terms of a mean mu
and sample size nu
:
alpha = mu * nu
beta = (1-mu) * nu