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Let be independent random variables, each having a distribution. If we try to estimate with an -state learning algorithm, then the minimum mean-squared error is bounded below by that obtained by the best -level quantizer (which requires knowledge of ). Here we show that this lower bound is tight. The results are easily extended to a number of other problems, such as estimating the mean of a uniform distribution.