Probabilistic Systems Analysis and Applied Probability

OCW Scholar

Images of chess, poker, dice, weather map, data stream, and a Markov chain.

Examples of the tools and applications of probability theory.




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Course Description

Course Features

Course Description

This course introduces students to the modeling, quantification, and analysis of uncertainty.  The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management.

Course Format

Click to get started. This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include:

  • Lecture Videos by MIT Professor John Tsitsiklis
  • Lecture Slides and Readings
  • Recitation Problems and Solutions
  • Recitation Help Videos by MIT Teaching Assistants
  • Tutorial Problems and Solutions
  • Tutorial Help Videos by MIT Teaching Assistants
  • Problem Sets with Solutions
  • Exams with Solutions

Related Resource

A complementary resource, Introduction to Probability, is provided by the videos developed for an EdX version of 6.041. These videos cover more or less the same content, in somewhat different order, and in somewhat more detail than the videotaped live lectures.

Other Versions

Related Content

John Tsitsiklis. 6.041SC Probabilistic Systems Analysis and Applied Probability. Fall 2013. Massachusetts Institute of Technology: MIT OpenCourseWare, License: Creative Commons BY-NC-SA.

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