A master’s thesis entitled “Bayesian and Intelligent Models for Estimating Transition Probability Matrices in Markov Chains: Applications to Sustainable Development Indicator Analysis” was discussed in the discussion hall of the College of Computer Science and Mathematics at the University of Mosul on Wednesday, June 10, 2026. The thesis was presented by student Rasoul Ali Hussein Al-Zaidy and supervised by Professor Dr. Muthanna Subhi Suleiman.

The thesis addressed the estimation of transition probability matrices in discrete-time Markov chains.

The study compared four methods: MLE, Bayesian-Dirichlet, contraction, and the ABC algorithm, under different data conditions.

The thesis aimed to obtain more accurate, stable, and dynamically reliable transition matrices.

This thesis contributes to the following Sustainable Development Goals:

1. Goal 4: Quality Education, through the application of the study to adult literacy rates.

2. Supporting the analysis of sustainable development indicators using applicable probability models.

The discussion committee consisted of:

Assistant Professor Dr. Zakaria Yahya Nouri (Chairman)

Assistant Professor Dr. Rikan Abdul Aziz Ahmed (Member)

Assistant Professor Dr. Munaf Hazem Ahmed (Member)

Prof. Dr. Muthanna Subhi Suleiman (Member and Supervisor)