Probabilistic Modeling for Conditional Statements

Abstract

A new mathematical framework is proposed in this
study to comprehend the impact of program architecture on
input random variables, the IF statement was the main topic. The
primary idea that is theoretically and experimentally supported
in this study is that the part of the joint pmf of a collection of
random variables that represents the condition will be shifted to
the part that represents the action. After sorting two random
variables, the framework is used with four random variables, and
the theoretically produced results were realistically validated.
The study's equations can be applied to assessing probabilistic
models of various sorting algorithms or other intricate program
structures. This may also result in future investigations
formalizing more precise execution time expectations.