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. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.