AI engineering glossary · Probability
What is markov's inequality?
Markov's inequality: For a non-negative random variable, the probability of exceeding a value a is at most its expected value divided by a: P(X >= a) <= E[X] / a. It is a crude but widely applicable bound that needs only the mean.
Markov's inequality: an example
If average page load is 2 seconds, at most 2 / 10 = 20% of loads can take 10 seconds or more.
Related terms
More Probability terms
- Maximum likelihood estimation
- Median
- Memoryless property
- Mode
- Monte Carlo simulation
- Monty Hall problem
- Multiplication rule
- Mutually exclusive events
Where you learn it
Markov's inequality is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.