Lecture 8: Tail Bounds // TRAIN BRAIN

Lecture 8: Tail Bounds

MIT 18.200 Principles of Discrete Applied Mathematics, Spring 2024
Instructor: Ankur Moitra
View the complete course: https://ocw.mit.edu/courses/18-200-principles-of-discrete-applied-mathematics-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61p2fXeXjNCrfNHFwyW-bl0
In this lecture we cover tail bounds; these bound the probability that a random variable is far away from the mean. We give Markov’s bound and Chebyshev’s bound. We then use Chebyshev’s bound to prove the weak law of large numbers.
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