Recognition of continuous probability models

It is well known that randomness is present in daily life and that often it is desirable to recognize inherent characteristics of this randomness. Probability theory describes a quantification of the uncertainty associated with this randomness. Based on probability theory, the present research describes an alternative methodology to the traditional statistical method of the recognition of the probabilistic models that best represent randomness. The main motivation of the methodology is to keep the largest possible amount of information present in the data. This methodology differs from the traditional statistical method, mainly in aspects related to the division of the data into classes when the data are continuous.

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