Probabilistic Analysis


Stanford School of Engineering


Probability theory is essential to many human activities that involve the quantitative analysis of large sets of data. This fast-paced course provides an understanding of uncertain phenomena using probability theory.

Develop conceptual and intuitive insights into probabilistic reasoning and the ability to understand and solve real world problems. Learn the concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication including the structuring, processing, and presentation of probabilistic information. Use spreadsheets to illustrate and solve problems to complement analytical closed-form solutions.


  • 1 year of college level calculus (through calculus of several variables, such as CME100)
  • Background in linear algebra
  • An undergraduate degree with a GPA of 3.0 or equivalent

Topics include

  • Axioms of probability
  • Probability trees
  • Random variables
  • Distributions
  • Conditioning
  • Expectation
  • Change of variables
  • Limit theorems

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.

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