Parametric modeling of PDFs assumes that the forms of the PDFs are known. Such knowledge typically
comes from either a scientific analysis of the physical process or from empirical analysis of the
observed data, e.g., a popular parametric PDF model for the noise in the k-space MRI data is
the independent and identically distributed (i.i.d.) additive Gaussian. Then what remains, in
statistical inference, is to estimate the parameters associated with the PDF. In many practical
situations, however, simple parametric models do not accurately explain the physical processes. One
reason for this is that virtually all the parametric PDF models are unimodal, but many practical
situations exhibit multimodal PDFs. Attempts at modeling high-dimensional multimodal PDFs as
products of 
D parametric PDFs do not succeed well in practice either. Therefore, one needs to
employ the more sophisticated nonparametric density-estimation techniques that do not make
any assumptions about the forms of the PDFs--except the mild assumption that PDFs are smooth
functions [171,156]--and can represent arbitrary PDFs given sufficient data. One
such technique is the Parzen-window density estimation.