Generally, stochastic (pronounced stow-KAS-tik, from the Greek stochastikos, or “skilled at aiming,” since stochos is a target) describes an approach to anything that is based on probability or chance occurrence. In science, a stochastic approach is one in which values are obtained from a corresponding sequence of jointly distributed random variables.

Classic examples of the stochastic process are guessing the length of a queue at a stated time given the random distribution over time of a number of people or objects entering and leaving the queue and guessing the amount of water in a reservoir based on the random distribution of rainfall and water usage. Ofcourse, a more classic example would be predicting the length of a queue of students lining up at Stanbic Bank, Makerere branch at a stated time given their random distribution over time for entry and exit from the queue after service by the banking officer or specialist.

To this point you will have realized the vast importance attached to the study, analysis of stochasticity in systems study. Many systems in one way or the other behave more stochastically than deterministically, hence the need to exploit stochasticity as an approach to programming for proper decision making, hence development. Proper understanding and application of stochastic computing in the field of statistics will build on the importance of metameta data that will enrich data and ease the process of data analysis to generate information for policy formultion and decision making.

Will continue next time and examine the meaning of stochastic programming… your comments are highly welcome…see u…