Linked from

The 20 pages that link to Independent and identically distributed random variables, each with the reason it gives.

• Maximum likelihood estimationRelated: This common sampling assumption makes the joint likelihood a product of individual contributions.

• Binomial distributionNarrower topic: The trial outcomes satisfy independence and have a common success probability.

• Law of large numbersRelated: This common setup supplies repeated observations with a shared expectation for classic versions of the theorem.

• Likelihood functionRelated: Under this common assumption, the sample likelihood factors into a product of individual observation probabilities or densities.

• Random walkRelated: Many basic walks are sums of repeated steps with this common independence structure.

• Spatial autocorrelationCompared with: Spatial autocorrelation violates the independence part of this common statistical baseline.

• Cross-validationRelated: Ordinary random folds assume observations are exchangeable under a shared data-generating process.

• Geometric distributionNarrower topic: Repeated trials meet this condition when each has the same success chance and does not affect the others.

• Likelihood ratioRelated: Under this assumption, the likelihood ratio for a sample factors into observation-level ratios.

• Strong law of large numbersRelated: This standard setting supplies a familiar version of the theorem's assumptions.

• Bootstrap methodRelated: The ordinary bootstrap most directly fits observations modeled as independent draws from one population.

• Degrees of freedomRelated: Many standard degrees-of-freedom formulas assume observations with this structure.

• Student's t-distributionRelated: Independence and a common normal distribution support the exact classical t result.

• Andrey MarkovCompared with: Markov’s work addressed sequences whose dependence violates this common simplifying assumption.

• Coupon collector's problemRelated: Each draw is modeled as an independent choice from the same uniform distribution.

• Wald's equationBroader topic: This standard sequence assumption combines the two key properties in Wald's equation.

• Glivenko–Cantelli theoremRelated: The classical theorem assumes observations drawn under this common sampling model.

• De Finetti's theoremNarrower topic: Each component of the mixture has exactly this conditional structure.

• Infinite monkey theoremNarrower topic: The standard model treats successive character choices as independent draws from one fixed distribution.

• Sample mean and covarianceRelated: This common sampling model yields standard unbiasedness and consistency results for the estimates.