MATH 4320 - Introduction to Stochastic Processes - University of Houston
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MATH 4320 - Introduction to Stochastic Processes

***This is a course guideline.  Students should contact instructor for the updated information on current course syllabus, textbooks, and course content*

Prerequisites: MATH 3338
Course Description: Generating functions, discrete and continuous versions of Poisson and Markov processes, branching and renewal processes, introduction to stochastic calculus and diffusion.
  • Required Text: An Introduction to Stochastic Modeling | Edition: 4. Mark Pinsky. Elsevier Science. ISBN: 978-0123814166 
  • Additional text: Introduction to Probability Models by Sheldon Ross. ISBN: 978-0124079489
Course Content: Dynamical processes throughout science and economics are often influenced by random fluctuations. Mathematically, a dynamical model that explicitly includes random fluctuations is a stochastic process. Math 4320 will introduce you to both the theory and the applications of stochastic processes. We will first review probability theory before examining new material. In particular, we will discuss background in probability theory with emphasis on conditional expectations and conditional distributions. Then we will cover more advanced topics such as discrete-time Markov chains, Poisson process, continuous-time Markov chains.
Grading & Make-up Policy/Assignment & Exam Details: Please consult your instructor's syllabus regarding any and all grading/assignment guidelines.

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