MIT OpenCourseWare

Browse materialsMIT 6.436J · Fall 2018

Fundamentals of Probability

Subjects: Management · Statistics

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

Instructor: Prof. Yury Polyanskiy
Term: Fall 2018
Level: Graduate
What's inside: 1 exam · 1 lecture note · 1 reading · 51 other files · 5 web pages
Materials open: MIT OCW

Start here

Start course: Lecture 1: Probabilistic Models and Probability Measures (PDF)Find a topic

Progress is saved in this browser.

Export or import progress

About this course

Source: MIT OpenCourseWare 6.436J Fundamentals of Probability, Fall 2018, Prof. Yury Polyanskiy — 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.

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

MIT's own materials here did not support a verified, course-wide order, so they are grouped by section instead.

Not included here

3 items from the source could not be safely included:

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

Materials by section

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

Readings

26 files

Recitation Notes

12 files

Assignments

12 files

Exams

MIT OpenCourseWare, Prof. Yury Polyanskiy, 6.436J Fundamentals of Probability, Fall 2018.

License: CC BY-NC-SA 4.0 · Edition: Fall 2018 · 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.

Fundamentals of Probability

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Recitation Notes

Assignments

Exams

Lecture 1: Probabilistic Models and Probability Measures (PDF)

Lecture 2: Two Fundamental Probabalistic Models (PDF)

Lecture 3: Conditioning and Independence (PDF).

Lecture 4: Random Variables (PDF)

Lecture 5: Discrete Random Variables and Their Expectations (PDF)

Lecture 6: More on Discrete Random Variables and Their Expectations (PDF)

Lecture 7: Abstract Integration I (PDF)

Lecture 8: Abstract Integration II (PDF)

Lecture 9: Product Measure and Fubini’s Theorem (PDF)

Lecture 10: Continous Random Variables I (PDF)

Lecture 11: Continous Random Variables II (PDF)

Lecture 12: Derived Distributions (PDF)

Lecture 13: Moment Generating Functions (PDF)

Lecture 14: Multivariate Normal Distributions (PDF)

Lecture 15: Multivariate Normal Distributions. Characteristic Functions (PDF)

Lecture 16: Convergence of Random Variables (PDF)

Lecture 17: Laws of Large Numbers I (PDF)

Lecture 18: Laws of Large Numbers II (PDF)

Lecture 19: Uniform Integrability. Convergence of Series (PDF)

Lecture 20: The Basics of Stochastic Processes (PDF)

Lecture 21: Markov Chains I (PDF)

Lecture 22: Markov Chains II (PDF)

Lecture 23: Markov Chains III (PDF)

Lecture 24: Infinite Markov Chains. Continuous Time Markov Chains (PDF)

Lecture 25: Martingales I (PDF)

Lecture 26: Martingales II (PDF)