# Uncertainty about Uncertainty: Optimal Adaptive Algorithms for Estimating Mixtures of Unknown Coins

## TL;DR

This paper establishes tight bounds on the number of samples needed for adaptive algorithms to estimate the proportion of positive coins in a mixture, with implications for crowdsourcing and distribution testing.

## Contribution

It provides the first tight sample complexity bounds for fully-adaptive algorithms in mixture coin estimation, including a novel decomposition lemma for adaptive bounds.

## Key findings

- Sample complexity is (rac{
ho}{\u03b5^2 \u2206^2}\, ext{log}rac{1}{\u03b4}) for success probability 1-.
- Lower bounds apply to all fully-adaptive algorithms, establishing tight dependence on parameters.
- Simulation results demonstrate practical effectiveness in crowdsourcing scenarios.

## Abstract

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 an upper and lower bound of $\Theta(\frac{\rho}{\epsilon^2\Delta^2}\log\frac{1}{\delta})$ samples for a $1-\delta$ probability of success, where crucially, our lower bound applies to all fully-adaptive algorithms. Thus, our sample complexity bounds have tight dependence for every relevant problem parameter. A crucial component of our lower bound proof is a decomposition lemma (see Lemmas 17 and 18) showing how to assemble partially-adaptive bounds into a fully-adaptive bound, which may be of independent interest: though we invoke it for the special case of Bernoulli random variables (coins), it applies to general distributions. We present simulation results to demonstrate the practical efficacy of our approach for realistic problem parameters for crowdsourcing applications, focusing on the "rare events" regime where $\rho$ is small. The fine-grained adaptive flavor of both our algorithm and lower bound contrasts with much previous work in distributional testing and learning.