Books

MIT OpenCourseWare

Browse materialsMIT 6.041SC · Fall 2013

Probabilistic Systems Analysis and Applied Probability

Subjects: Engineering · Mathematics · Statistics

Art: not stated, Fletcher's Combined Block Telegraph Apparatus and Bell, 1889 · Public domain

Instructor
Prof. John Tsitsiklis
Term
Fall 2013
Level
Undergraduate
What's inside
177 videos · 11 problem sets · 14 exams · 66 solutions · 308 other files · 34 web pages
Materials open
Videos here · files on MIT OCW
Other versions of this course

Start here

Start course: Lecture 1: Probability Models and Axioms VideoFind a topic

Progress is saved in this browser.

Export or import progress
About this course

Source: MIT OpenCourseWare 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013, Prof. John Tsitsiklis — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Learn one topic

20 concept pages, one per MIT session: the clips, notes and practice for that topic. MIT solutions for 20 of 20 topics.

All 20 topics
  1. Probability Models and Axioms MIT solutions
  2. Conditioning and Bayes' Rule MIT solutions
  3. Independence MIT solutions
  4. Counting MIT solutions
  5. Discrete Random Variables; Probability Mass Functions; Expectations MIT solutions
  6. Discrete Random Variable Examples; Joint PMFs MIT solutions
  7. Multiple Discrete Random Variables MIT solutions
  8. Continuous Random Variables MIT solutions
  9. Multiple Continuous Random Variables MIT solutions
  10. Continuous Bayes' Rule; Derived Distributions MIT solutions
  11. Derived Distributions; Convolution; Covariance and Correlation MIT solutions
  12. Iterated Expectations; Sum of a Random Number of Random Variables MIT solutions
  13. Bernoulli Process MIT solutions
  14. Poisson Process MIT solutions
  15. Markov Chains MIT solutions
  16. Weak Law of Large Numbers MIT solutions
  17. Central Limit Theorem MIT solutions
  18. Bayesian Statistical Inference MIT solutions
  19. Classical Statistical Inference - I MIT solutions
  20. Classical Inference MIT solutions

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

What's here, and what isn't

  • This course has 99 videos, each embedded on its own MIT page, across 26 sections. They're shown in MIT's listing order within each section. A verified course-wide order is not claimed for this course.

Not included here

9 items from the source could not be safely included:

  • Referenced in a table, but without MIT's own verified order, so it isn't used to claim a sequence. (7 items)
  • MIT's page format for this item wasn't recognized clearly enough to place it safely. (2 items)

Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Unit I: Probability Models And Discrete Random Variables

50 files in this section

Lecture 1

Lecture 2

Lecture 3

7 files

Lecture 4

Lecture 5

9 files

Lecture 6

Lecture 7

7 files

Quiz 1

9 files

Unit II: General Random Variables

38 files in this section

Lecture 8

Lecture 9

7 files

Lecture 10

Lecture 11

Lecture 12

9 files

Quiz 2

7 files

Unit III: Random Processes

32 files in this section

Lecture 13

Lecture 14

Lecture 15

7 files

Lecture 16

Lecture 17

Lecture 18

Unit IV: Laws Of Large Numbers And Inference

29 items in this section

Lecture 19

7 files

1 video

Lecture 20

Lecture 21

Lecture 22

Lecture 23

Lecture 24

Lecture 25

Resource Index

6 files

50 videos

Other materials from this course

243 files
26 videos
34 images

MIT OpenCourseWare, Prof. John Tsitsiklis, 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013.

License: CC BY-NC-SA 4.0 · Edition: Fall 2013 · Open the original course on OCW ↗

Organized by RedLectern, independent of MIT and not endorsed by MIT.

Per-resource rights are recorded on each resource; All-Rights-Reserved items are link-only, never mirrored.