Given a mixture between two populations of coins, "positive" coins that each have---unknown and potentially different---bias $\geq\frac{1}{2}+\Delta$ and "negative" coins with bias $\leq\frac{1}{2}-\Delta$, we consider the task of estimating the fraction $\rho$ of positive coins to within additive error $\epsilon$. We achieve a tight upper and lower bound of $\Theta(\frac{\rho}{\epsilon^2\Delta^2})$ samples for constant probability of success... (read more)

PDF
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.

METHOD | TYPE | |
---|---|---|

🤖 No Methods Found | Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet |