LLN

E582381

LLN is a fundamental theorem in probability theory stating that as the number of independent, identically distributed trials increases, the sample average converges to the expected value.

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Label Occurrences
LLN canonical 1

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Statements (38)

Predicate Object
instanceOf law of large numbers ⓘ
probability theorem ⓘ
alsoKnownAs strong law of large numbers ⓘ
weak law of large numbers ⓘ
appliesTo independent identically distributed random variables ⓘ
assumes finite expected value ⓘ
identical distribution of trials ⓘ
independence of trials ⓘ
category limit theorem in probability ⓘ
contrastedWith central limit theorem ⓘ
describes convergence of sample averages to expected value ⓘ
ensures stability of long-run frequencies ⓘ
field probability theory ⓘ
statistics ⓘ
formalization measure-theoretic probability framework ⓘ
formalizes long-run average behavior of random experiments ⓘ
fullName law of large numbers ⓘ
hasConsequence empirical mean is consistent estimator of expectation ⓘ
hasVariant strong law of large numbers ⓘ
weak law of large numbers ⓘ
historicalOrigin Jakob Bernoulli ⓘ
holdsUnder certain moment conditions ⓘ
implies law of averages in colloquial terms ⓘ
sample mean approximates population mean ⓘ
mathematicalObject theorem about sequences of random variables ⓘ
motivates use of sample averages in statistics ⓘ
provenBy Chebyshev ⓘ
Kolmogorov ⓘ
linked to: Andrei Kolmogorov
relatedConcept Borel–Cantelli lemma ⓘ
Chebyshev inequality ⓘ
ergodic theorem ⓘ
requires large number of trials ⓘ
typeOfConvergence almost sure convergence ⓘ
convergence in probability ⓘ
usedIn Monte Carlo methods ⓘ
frequentist interpretation of probability ⓘ
statistical inference ⓘ
yearFirstFormulated early 18th century ⓘ

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Referenced by (1)

Full triples — surface form annotated when it differs from this entity's canonical label.