Probability theory and stochastic processes Inequalities Theorem
Markov's inequality
Statement
Let be a random variable with values in and let . Then
Remarks
Proof: pointwise. Take expectations: . More generally, for each nondecreasing with , . Applied to , the inequality gives Chebyshev's inequality. Applied to , it gives the Chernoff bound.
History
The inequality is named after Andrey Markov. It appears earlier in the work of Pafnuty Chebyshev, the teacher of Markov.
Reading path
To understand this page from the ground up, read these 45 pages in order. The foundations come first.
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Complement definition
- Function definition
- Interval definition
- Injective function definition
- Preimage of a set definition
- Sigma-algebra definition
- Countable set definition
- Event definition
- Borel sigma-algebra definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Monotonicity of probability statement
- Random variable definition
- Discrete random variable definition
- Random variable with a density definition
- Expected value definition
- Markov's inequality statement
Where this leads
18 pages build on this one.
- Chernoff bound
- Chebyshev's inequality
- Weak law of large numbers
- Strong law of large numbers
- Hoeffding's inequality
- Ergodic decomposition
- Koksma–Hlawka inequality
- Doob's martingale inequality
- Consistency of the maximum likelihood estimator
- Kingman's subadditive ergodic theorem
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Azuma's inequality
- Monte Carlo integration
- Birkhoff's ergodic theorem
- Bernstein inequalities
- Oseledets theorem
- Pesin theory
Further reading
About this page
| Tag | 05PWWHYBX5 |
|---|---|
| URI | m2://05PWWHYBX5 |
| Kind | statement, theorem |
| Writing | W1 stated |
| MSC | 60E15 |
| Status | This copy of the site has no build data. Run m2 status 05PWWHYBX5. |
| Uses | Expected value, Monotonicity of probability |
| Same as |
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What this page needs for W2
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- edge proves in: at least one proof node (0 of 1)
- edge instance_of or illustrates in: one example that shows the statement at work (0 of 1)
- explanation L2 (0 of 1)
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