 Bootstrapping can be interpreted in a Bayesian framework using a scheme that creates new data sets through reweighting the initial data.Given a set of formula_5 data points, the weighting assigned to data point formula_6 in a new data setformula_7 is formula_8, where formula_9 is a low-to-high ordered list of formula_10 uniformly distributed random numbers on formula_11, preceded by 0 and succeeded by 1.The distributions of a parameter inferred from considering many such data sets formula_7 are then interpretable as posterior distributions on that parameter.