and then estimates the parameters of the assumed distribution from the data. This approach has advantages as long as
the distributional assumption is correct. The main disadvantage of the parametric approach is the lack of flexibility. Each
parametric family of distributions constrains the shapes of f(x). The idea of the non-parametric approach is to avoid
restrictive assumptions about the form of f(x) and to estimate this directly from the data. A well-known non-parametric
estimator of the pdf is the histogram. It is simple but it is not continuous. Furthermore, the choice of the bin width could
significantly affect the shape of f(x).
Alternatively, the pdf can be estimated by means of the kernel density estimator technique [20,21], which approximates
the density function as