MIT OpenCourseWare

Browse materialsMIT 18.338J · Fall 2004

Infinite Random Matrix Theory

Subjects: Algebra · Linear Algebra · Mathematics · Physics · Statistics

Art: Max Brückner (1860 – 1934), Brückner Vielflache Tafel 07 · Public domain

Instructors: Prof. Moe Win, Prof. Alan Edelman
Term: Fall 2004
Level: Graduate
What's inside: 1 lecture note · 1 reading · 34 other files · 4 web pages
Materials open: MIT OCW

Start here

Start course: Lecture 1: Introduction Find a topic

Progress is saved in this browser.

Export or import progress

About this course

Source: MIT OpenCourseWare 18.338J Infinite Random Matrix Theory, Fall 2004, Prof. Moe Win, Prof. Alan Edelman — 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

A lecture sequence of 27 items is confirmed. No assignments or exams were found for this course, so it stays as organized materials rather than a full course order.

Not included here

74 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. (74 items)

Materials by section

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

Readings

Lecture Notes

19 files

Other materials from this course

13 files

MIT OpenCourseWare, Prof. Moe Win, Prof. Alan Edelman, 18.338J Infinite Random Matrix Theory, Fall 2004.

License: CC BY-NC-SA 4.0 · Edition: Fall 2004 · 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.

Infinite Random Matrix Theory

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Other materials from this course

Lecture 1: Introduction

Lecture 2: The Hermite Ensemble: Wigner’s Semi-Circle Law

Lecture 3: The Laguerre Ensemble: Marcenko-Pastur Theorem

Lecture 4: The Jacobi Ensemble: McKay’s Random Graph Theorem

Lecture 5: The “Semi-Circular” Element: Central Limit Theorem for Infinite Random Matrices

Lecture 6: Free Cumulants in Free Probability

Lecture 7: Free Cumulants in Free Probability (cont.)

Lecture 8: Non-Crossing Partitions and Free Cumulants

Lecture 9: Free Cumulants: The Semi-Circular and “Free Poisson” Distributions

Lecture 10: Additive Free Convolution

Lecture 11: The R-Transform and the Marcenko-Pastur Theorem

Lecture 12: Multiplicative Free Convolution

Lecture 13: The S-Transform

Lecture 14: Dr. Anna Scaglione Talk

Lecture 15: Orthogonal Polynomials and the Classical Matrix Ensembles

Lecture 16: Project Progress Presentation

Lecture 17: Project Progress Presentation (cont.)

Lecture 18: Tracy Widom Distribution

Lecture 19: Eigenvalue Spectrum Fluctuations

Lecture 20: Dr. Roland Speicher Talk

Lecture 21: Free Probability and Fluctuations

Lecture 22: Zonal Polynomials and Random Matrices

Lecture 23: Symmetric Group Representations and Free Probability

Lecture 24: MOPs

Lecture 25: Connections and Open Problems

Lecture 26: Project Presentations

Lecture 27: Project Presentations (cont.)