Restoration of distribution density by kernel functions from sampled data

A way to estimate unknown distribution density is suggested whereby the distribution is represented as a linear combination of functions of kernels. The linear combination coefficient are found on the knowledge of the minimum of the weighted r.m.s. error of the desired distribution, the error being found from sampled values. These coefficients converge almost surely to their mean values as the sample graws.