Lecture 8: Tail Bounds

MIT 18.200 Principles of Discrete Applied Mathematics, Spring 2024
Instructor: Ankur Moitra
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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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