↗Cheat-sheet: tail bounds (Markov, Chebyshev, Chernoff) with when to use each

A one-pager I made for the probability part of the course:

• Markov — only needs $\mathbb{E}[X]$, weakest bound.
• Chebyshev — needs variance, good for concentration around the mean.
• Chernoff — sums of independent indicators, exponentially strong.

Includes worked examples for analysing probabilistic algorithms.