Setup
Suppose I have two normally distributed random variables, $s_\mathrm{prev}$ (with mean $\mu_\mathrm{prev}$ and variance $\sigma^2_\mathrm{prev}$) and $s_\mathrm{curr}$ (with mean $\mu_\mathrm{curr}$ and variance $\sigma^2_\mathrm{curr}$). Now suppose I combine them as follows:
$r_\mathrm{curr} = \omega_\mathrm{prev}s_\mathrm{prev}+(1-\omega_\mathrm{prev})s_\mathrm{curr}$,
where $\omega_\mathrm{prev}$ lies between 0 and 1.
The mean of $r_\mathrm{curr}$ is $\omega_\mathrm{prev}\mu_\mathrm{prev} + (1-\omega_\mathrm{prev})\mu_\mathrm{curr}$.
Question
What is the variance of $r_\mathrm{curr}$?
According to the answers here, the variance is $\omega_\mathrm{prev}\sigma^2_\mathrm{prev}+(1-\omega_\mathrm{prev})\sigma^2_\mathrm{curr}+\omega_\mathrm{prev}(1-\omega_\mathrm{prev})(\mu_\mathrm{prev}-\mu_\mathrm{curr})^2$.
But these answers refer to a mixture of two normal pdfs, not random variables.
I've also seen it said that the variance I'm looking for is $\omega^2_\mathrm{prev}\sigma^2_\mathrm{prev}+(1-\omega_\mathrm{prev})^2\sigma^2_\mathrm{curr}$. If this is correct, how is this derived?