حل سلاسل ماركوف بالمصفوفة العشوائية

الملخص EN

This research deals with the study of the Markov process, which is a random indicator model that contains a Markov property and can be used to design a random model that changes according to the transformation rule.

Markov processes are used in many areas, including: machine learning (reinforcement learning), pattern recognition, disease diagnosis and treatment decisions, in particular The pointer of the parameter state, space, and time of the system needs to be specified, and there are different states of the Markov process for different levels of states in general and for discrete versus continuous time.

Markov chains have many applications in computing and Internet technologies.

The main problem of Markov process decisions is to find the policy for the decision maker, and it aims to choose the policy that maximizes some cumulative functions for random cases, and Markov process decisions can be solved through linear programming and dynamic programming.

Where Markov chains and their properties were studied, and the stochastic matrix and its types, Markov chains were applied in the random matrix using the (Maple) program, the eigenvalues and eigenvectors were found and their stability was resolved