This is an introductory course of stochastic processes. In this course, different types of modeling and analysis of practical phenomena in terms of stochastic processes will be introduced. The content of this course include basic stochastic processes, stochastic models, and diffusion processes. The course covers the following topics：Markov models (including Poisson processes, discrete-time and continuous-time Markov chains), renewal processes, and Brownian motion etc.

The objective of this course is to introduce basic concepts for stochastic processes. The main focus will be on studying analytical models for systems which change states stochastically with time. The topics include: 1. Markov Chains 1A. Hidden Markov Models and Related Topics 2. Poisson processes 2A. Non-homogeneous Poisson processes and Related Topics 3. Continuous-Time Markov Chain 3A. Queueing Models 4. Brownian motion and Martingales 4A. Black-Scholes Models and Related Topics 5. Linear State Space Model 5A Filtering

1. Sheldon M. Ross (2014) Introduction to Probability Models, 11th ed, Academic Press
2. Sheldon M. Ross (1996) Stochastic Processes, 2nd ed, John Wiley.
3. Chung K.L. and Williams R.J. (1990) Introduction to Stochastic Integration (2/E), Birkhäuser.