Abstract
Given a set of points drawn from an unknown continuous probability distribution, one often wishes to infer the underlying distribution. This distribution can be estimated using a simple scalar field theory. Fluctuations around the estimate are characterized by a robust measure of goodness of fit, analogous to the conventional ${\ensuremath{\chi}}^{2}$, whose distribution can also be calculated. The resulting method of data analysis has some advantages over conventional approaches.
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