Introduction to Stochastic Processes and Computer Simulation

 
 

The ability to model systems under uncertainty is an important skill. The ubiquitous nature of Markov Chain applications makes it very important in a diverse range of subjects, such as bioinformatics, industrial engineering, telecommunications, finance, strategic planning and manufacturing.


This course addresses that need by studying fundamental results of Markov chain processes. The focus is on modeling and many examples will be covered. In real problems, often analytical solutions are impossible to obtain, mainly (but not only) due to large state spaces. Simulation is a versatile and popular tool that can provide numerical approximations. This course covers topics of computer simulation and modeling that emphasize statistical design and interpretation of results.

 

Learning Objectives

Course Description

 

This course covers probability models, with emphasis on Markov chains. Theoretical results will be stated, and focus is on modeling. The last part of the course is devoted to techniques and methods of simulation, with emphasis on statistical design and interpretation of results. Students will work in team projects with a programing component.


The students who succeed this course will:


  1. Bulletunderstand and apply probability models to describe real problems,


  1. Bulletbe capable of designing computer simulations for Markov chains,


  1. Bulletunderstand how to interpret and present the statistical results from simulations, and


  1. Bulletunderstand the analysis techniques for studying Markov chains.