Stochastic Process Approaches for Modeling of Games

Abstract

The article detailed how stochastic process models are used to analyze game progression and included examples to illustrate the process. The issue was examined using the idea of a game; stochastic models were built, and transition matrix probabilities were estimated using the results obtained. As a result, win and loss can be used to indicate the states. The models that have been provided make it possible to identify and examine the interactions among the collection of system states. For the purpose of modeling events and computing transition probabilities between states, Markov chains and Hidden Markov Models are employed. The suggested frameworks enable the prediction of future states and allow for the differentiation of game phases of flow.