Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

MIT OpenCourseWare

Linearity of Expectations in Combinatorics - Applications to Permutations and Hamilton Paths

MIT OpenCourseWare via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Learn about combinatorial applications of linearity of expectations in this mathematics lecture from MIT's Probabilistic Methods in Combinatorics course. Explore two key examples - calculating the number of fixed points in random permutations and analyzing Hamilton paths in tournaments - to understand how expectation linearity principles can solve complex combinatorial problems. Through clear explanations by Professor Yufei Zhao, master fundamental concepts that bridge probability theory and combinatorial mathematics.

Syllabus

Linearity of Expectations

Taught by

MIT OpenCourseWare

Reviews

Start your review of Linearity of Expectations in Combinatorics - Applications to Permutations and Hamilton Paths

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.