I got an initial mean $\mu_1$ and std $\sigma_1$ by sampling samples, these samples are generated by an unknown distribution and later I drop these samples. Then I sampled some samples and got the mean $\mu_2$ and std $\sigma_2$ from the new sample and I kept the new samples. So how can I get the new std?
My idea is $(\sigma_1+\sigma_2)/2$ but I think this result is biased.
Another idea is $\mu_3=(\mu_1+\mu_2)/2$, and I reconstruct the previous samples by making an array with all items are $\mu_1$. Then combine the previous samples with the new samples and use the $\mu_3$ to get the $\sigma_3$.
Update: I clarify my question
I want to estimate mean and variance from unknow sample, due to memory cost, I can sample the data and get the mean and variance and then drop these samples, and then I sample data from the distribution and compute the mean and variance of the new samples, before dropping the new sample, I want to estimate the mean and variance of the distribution based on the current mean and variance and that of the previous step.