Kolmogorov's law of large numbers
GPTKB entity
Statements (55)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:theorem
|
| gptkbp:appliesTo |
independent random variables
|
| gptkbp:description |
convergence of sample averages
|
| https://www.w3.org/2000/01/rdf-schema#label |
Kolmogorov's law of large numbers
|
| gptkbp:is_a |
mathematical_statistics
|
| gptkbp:is_a_platform_for |
Bayesian statistics
empirical research central limit theorem Monte_Carlo_methods |
| gptkbp:is_a_subject_of |
probability theory
statistical mechanics the study of stochastic processes statistical theory theoretical statistics provides a framework for estimation demonstrates the power of large samples. ensures reliability of statistical conclusions facilitates decision-making under uncertainty provides insight into sampling distributions underpins many statistical methods |
| gptkbp:is_essential_for |
economics
risk assessment predictive modeling hypothesis testing random processes social_sciences large sample theory law of large numbers applications understanding variability in data |
| gptkbp:is_studied_in |
long-term behavior of averages
|
| gptkbp:is_used_in |
machine learning
quality control data science statistical inference experimental design sample size determination probability measures |
| gptkbp:legal_principle |
data analysis
quantitative research supports empirical validation of theories |
| gptkbp:previousName |
gptkb:Andrey_Kolmogorov
|
| gptkbp:publishedBy |
1933
|
| gptkbp:related_to |
asymptotic analysis
Bernoulli trials convergence in probability strong law of large numbers weak law of large numbers |
| gptkbp:requires |
finite expected value
|
| gptkbp:state |
the sample average converges to the expected value
|
| gptkbp:suitableFor |
financial modeling
health statistics gambling theory insurance mathematics |
| gptkbp:was_a_result_of |
measure theory
independence of random variables |