高斯条件下概率分布的最大熵估值估计
Maximum entropy estimation of probability distributions with Gaussian conditions
摘要：
利用最大熵原理，在局部条件由高斯函数给出的条件下，给出了一元随机变量的密度函数的计算估计方法.确定了参数的估计误差和最优值。给出了实验结果。如果使用足够大的选择，通常至少1000个值，则该方法可以很好地估计分布。与熵最大化的经典方法相比，局部条件允许局部地改进估计。该方法非常适合于启发式优化方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
