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Stochastic processes and row models

Course Description: Definition of random processes - Markov property - Markov chains - One-step and multi-step transition probability matrices - Kolmogorov-Chapman equations - Classification of states - Stationary distributions of Markov chains - Continuous-time Markov processes (birth and death processes - Poisson process) - Queueing models and their elements: Study and evaluation of queueing systems using cumulative diagrams - Performance measures - Simple Markovian queueing models (M/M/1 - M/M/c - and finite capacity) - Some non-Markovian queueing systems - Some Markovian models with batch arrival and batch service.
Credit hours: 4
Prerequisites: OPER 201
Objectives of the course :

This course aims to build upon the student's knowledge of Markov chains, their distributions, queuing models, and performance measures.

Course outputs :

Acquiring knowledge by learning some basic methods and concepts related to stable distributions of Markov chains. Enhancing the ability to use and apply statistical methods for queuing models and decision-making in relevant fields. Developing cognitive skills through thinking and solving real-life problems. Improving the capacity for further advanced studies when needed in the field of applied statistics and operations research in general.

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  • Last updated: 2026-06-04

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