Probability theory and stochastic processes Limit theorems Theorem
Strong law of large numbers
Builds on Independent and identically distributed random variablesAlmost sure convergenceWeak law of large numbersLaw of large numbers
Statement
Let be i.i.d. real random variables with and . Let . Then almost surely:
Remarks
The strong law implies the weak law, because almost sure convergence implies convergence in probability. A short proof exists if : then , and the first Borel–Cantelli lemma applies. Etemadi proved that the result holds when the are only pairwise independent and identically distributed. Conversely, if , then almost surely does not converge to a finite limit.
Reading path
To understand this page from the ground up, read these 78 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 bounded above definition
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Supremum definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Completeness axiom statement
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Existence of square roots of nonnegative reals statement
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Square root 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 distribution definition
- Probability space definition
- Almost surely definition
- Conditional probability definition
- Monotonicity of probability statement
- Random variable definition
- Independence of two events definition
- Almost sure convergence definition
- Discrete random variable definition
- Law of a random variable definition
- Random variable with a density definition
- Convergence in probability definition
- Mutual independence of events definition
- Independence of two random variables definition
- Joint probability distribution definition
- Expected value definition
- Identically distributed random variables definition
- Independent sigma-algebras definition
- Marginal distribution definition
- Markov's inequality statement
- Linearity of expectation statement
- Variance definition
- Independence of random variables definition
- Independence means the joint law is the product of the marginals statement
- Covariance definition
- Standard deviation definition
- Expectation of a product of independent random variables statement
- Independent and identically distributed random variables definition
- Chebyshev's inequality statement
- Variance of a sum statement
- Independent random variables are uncorrelated statement
- Law of large numbers concept
- Variance of a sum of independent random variables statement
- Weak law of large numbers statement
- Strong law of large numbers statement
Where this leads
7 pages build on this one.
Further reading
About this page
| Tag | 0DFWHQ839T |
|---|---|
| URI | m2://0DFWHQ839T |
| Aliases | kolmogorov-strong-law |
| Kind | statement, theorem |
| Writing | W1 stated |
| MSC | 60F15 |
| Status | This copy of the site has no build data. Run m2 status 0DFWHQ839T. |
| Uses | Independent and identically distributed random variables, Almost sure convergence, Weak law of large numbers, Law of large numbers |
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