1. A method of analyzing a measurable distribution having random components and deterministic components, comprising the steps of: (a) collecting data from a data source; (b) constructing a probability density function based on the data such that the probability density function defines a distribution, wherein the probability density function is a convolution of deterministic functions and random functions; (c) construct a probability density function based on a convolution model having three or more parameters wherein at least one of the parameters are unknown, the convolution model having a deterministic model and a random model; (d) determining unknown parameters by using a deconvolution process.