Let $ X_{1},…,X_{d} \in \{-1,1\}^d$ be random variables, with $ E[X_j]=\mu_j$ . Having $ n$ i.i.d. samples $ x^{(i)}_1,x^{(i)}_2,….,x^{(i)}_d$ , $ i=1,…,n $ , let $ \hat{\mu}_{j}=\frac{1}{n}\sum^{n}_{i=1}x^{(i)}_j$ Then we would like to find an upper bound for $ \text{Pr}[|\prod^{d}_{i=1}\hat{\mu}_{j}-\prod^{d}_{i=1}\mu_{j}|\geq \epsilon]$ where $ \epsilon>0$ . We have $ \prod^{d}_{j=1}\hat{\mu}_{j}=\prod^{d}_{j=1}(\frac{1}{n}\sum^{n}_{i=1}x^{(i)}_j)=\frac{1}{n^d}\prod^{d}_{j=1}\sum^{n}_{i_j=1}x^{(i_j)}_j=\frac{1}{n^d}\sum^{n}_{i_1,…,i_d=1}\prod^{d}_{j=1}x^{(i_j)}_j$ . The random variables $ \prod^{d}_{j=1}x^{(i_j)}_j$ areRead more