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Applied Stochastic Processes
Release Date: 2017-09-221. Basic Course Information
Course Title:Applied Stochastic Processes
Course Code:102413
Credit Hours/Credits:48/3
Students:Undergraduate Students majoring in Statistics and Applied Statistics
Prerequisites:Probability Theory,Mathematical Statistics and Applied Regression Analysis
Subsequent Course(s):None
Course Category:Professional Foundamental Course
Assessment: final exam (70%) + Participation & Assignments (30%)
2. Course Introduction
Stochastic processes are essentially probabilistic systems that evolve in time. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes, The range of areas for which stochastic-process models are useful includes many applications in engineering, finance and economics. The content includes: Poisson process, renew process, Markov chain of dispersed time, Markov chain of continuous time, stochastic differential equation, martingale and application of Brownian motion and stochastic integral.