This course offers a comprehensive introduction to stochastic modeling, a powerful analytical tool used to represent complex, uncertain phenomena. The course is geared towards graduate students with a background in calculus, probability, and statistics. Throughout the term, we'll explore fundamental concepts and methods in stochastic modeling, including Markov chains, Poisson processes, queuing systems, and simulation techniques. We'll examine their applications in various fields, such as finance, engineering, computer science, and operations research. Students will gain hands-on experience through solving real-world problems, developing their skills in probabilistic reasoning and statistical inference. Emphasis will be placed on interpreting and communicating stochastic model results effectively, empowering students to make data-informed decisions in uncertain environments.
Understand Core Concepts: Students will develop a comprehensive understanding of stochastic processes, Markov chains, Poisson processes, queuing models, and other core concepts in stochastic modeling.
Application in Various Fields: Students will gain an appreciation for the broad application of stochastic models across different industries and disciplines, learning to apply these techniques in areas like finance, operations research, engineering, and computer science.
Problem-Solving Skills: Through real-world case studies and projects, students will hone their problem-solving skills, learning to develop and use stochastic models to address complex, uncertain situations.
Statistical Inference: Students will gain a solid understanding of statistical inference techniques as applied to stochastic models, learning to make robust predictions and decisions in the face of uncertainty.
Simulation Techniques: The course will equip students with the practical skills to perform simulations using stochastic models, an essential component in fields like computational finance or supply chain management.
Effective Communication: Students will learn to communicate the outcomes of their stochastic modeling effectively, being able to explain their models, the process of development, and the implications of their results to both technical and non-technical stakeholders.
In-class lectures and tutorials
Written exam and assigments