Parametric probability density estimation is a technique that involves selecting a common distribution and estimating the parameters for the density function from a data sample. In this paper, we are dealing with an experiment in which it is required to find a probability density function. We start by simulating the experiment considered in order to obtain information about the behavior of the system and further assign certain values such as mean and standard derivation for the sample’s distribution, which will be obtained based on certain assumptions rather than randomly selecting trivial values. After obtaining a relatively rational sample of a number of experiments, we assume no previous knowledge of the data sample and try to estimate its probability density function using parametric density estimation. Finally, we give some perspective for a further comprehensive approach and end with a conclusion of the work.
Density Estimation, Probability, Random Variable, Simulation.
The work in this paper is not officially published.