A neural network method of density estimation for univariate unimodal data

In real world statistical applications a large class of univariate probability distributions are unimodal. This paper presents a neural network approach to probability density estimation for univariate unimodal data. This approach is superior to the traditional statistical density estimation methods in that neither of the forms of probability function (as in the parametric methods) or the kernel functions (as in the non-parametric methods) is needed to form an a prioriassumption. An example is used to demonstrate that the proposed approach is an effective method for estimating univariate unimodal probability distributions.