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Maximum entropy estimation of the probability density function from the histogram using order statistic constraints

An analytical expression for a probability density is usually required in detection and estimation problems, yet it is usually only assumed or selected from contenders by parameter estimation, or the histogram is smoothed with an arbitrary window function. In contrast, given a histogram containing R sample points, we derive a nonlinear differential equation (NDEQ) whose solution is a maximum entropy density given constraints that arise from assumptions that the samples are means of the order statistics of the parent distribution. We solve the NDEQ for R=1 and approximate the solution for general R using the fact that order means partition the density into equal probability regions, which we require to independently be maximum entropy. Finally we show with a Rayleigh density example what errors may result.