Introduction to Stochastic Processes I
Fee may apply
A stochastic process is a set of random variables indexed by time or space. Stochastic modelling is an interesting and challenging area of probability and statistics that is widely used in the applied sciences. In this course you will gain the theoretical knowledge and practical skills necessary for the analysis of stochastic systems. You will study the basic concepts of the theory of stochastic processes and explore different types of stochastic processes including Markov chains, Poisson processes and birth-and-death processes.
Non-Statistics master’s students may want to consider taking STATS 215 instead.
What you will learn
- The standard concepts and methods of stochastic modeling
- How to choose the best stochastic process for specific situations
- How to apply stochastic analysis to realistic problems
- Anthony D'Aristotile
A post-calculus introductory probability course, e.g. Stanford Course STATS116
- Discrete and continuous time Markov chains
- First step analysis: gambler’s ruin and successful runs
- Branching processes
- Poisson processes
- Birth-and-death processes
- Long run behavior
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: April 14 to June 17, 2019
This course may not currently be available to residents of certain states.