  We describe a method to computationally estimate the probability density
function of a univariate random variable by applying the maximum entropy
principle with some local conditions given by Gaussian functions. The
estimation errors and optimal values of parameters are determined. Experimental
results are presented. The method estimates the distribution well if a large
enough selection is used, typically at least 1 000 values. Compared to the
classical approach of entropy maximisation, local conditions allow improving
estimation locally. The method is well suited for a heuristic optimisation
approach.
