Estimating the parameters of gaussians to fit a lot of samples can be do with...

Estimating the parameters of gaussians to fit a lot of samples can be do with Exceptation Maximization, for instance if we want to fit two gaussian on points, to have the clusters $a$ and $b$. (1)
$b_i=P(b|x_i)=\frac{P(x_i|b)P(b)}{P(x_i|b)P(b)+P(x_i|b)P(a)}$
Here $P(b)$ is the prior that depicts the overall importance of the $b$ cluster.
This prior is then updated for the next step, according on how many the points belongs to the $b$ cluster:
$P(b)=\frac{1}{n}\sum _i{b}_i$
However, what is the value of the prior $P(b)$ on the first iteration of the algorithm?