Another way to determine the distribution is to estimate individual quantiles, starting with the estimation of median, then 25 and 75% quantiles and these intervals are again divided into a half. Quantiles are then intersected by a curve representing the searched distribution function which derivative is the searched density (Bartosova, 2008). However, these functions are very sensitive to changes of individual values and the inaccuracies can also bring a kind of interpolation function.
On the other hand, if there are some historical data at disposal, we can construct the probability distributions based on it, either by parametric or non-parametric methods.
Parametric method is to be defined only very briefly as main focus of this article is given to the non-parametric one. Parametric methods assume that the observed data comes from some known distribution with unknown parameters, which are estimated thanks to this data (Misankova et. al., 2014). Correctness of the choice of a particular distribution is then tested, for example. The most often used types of distribution are: normal, logistic, lognormal, gama and Pareto distributions (Cisko, Kliestik, 2013).