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Introduction to Stochastic Processes


Markov chains; Markov processes; diffusion processes; stationary processes. Emphasis is placed on applications; random walks; branching theory; Brownian motion; Poisson process; birth and death processes.


Stochastic Processes, Ross, Wiley, 2nd edition.


MATH 765 or permission of the instructor.

Credit Hours: 

A stochastic process is a collection of random variables depending on a discrete or continuous time parameter. Stochastic or random processes are mathematical models for empirical date in a variety of areas as medicine, biology, physics, engineering, economics and psychology. The aim of this course is to study some basic examples of stochastic processes.


  1. Markov chains.
  2. Martingales with discrete time.
  3. Poisson processes.
  4. Continuous-time Markov chains.
  5. Renewal theory.
  6. Brownian motion.

(Pasik-Duncan 2009 )

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