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Independent random variables

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Summary

A family of random variables whose finite subfamilies have joint probabilities that factor into their marginal probabilities.

Record metadata

Carrier(s)

Axioms / constraints

Canonically induces

Notes

Pairwise independence is weaker than mutual independence and is not substituted here.

Concept sources

Incoming relations (arrows to this concept)

Each relation below ends at this concept.

Stochastic processIndependent random variables

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This is an authored directed relation from the source endpoint to the target endpoint.

Authored explanation

Require every finite subfamily of the indexed random variables to have joint probabilities that factor into their marginals.

How to interpret this relation type

Keep the existing data and select the subclass satisfying an additional law, existence condition, finiteness condition, or other property.

Relation sources

Outgoing relations (arrows from this concept)

Each relation below starts at this concept.

Independent random variablesCentral limit theorem

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This is an authored directed relation from the source endpoint to the target endpoint.

Authored explanation

For iid real variables with finite nonzero variance, centered and normalized sums converge in distribution to a standard normal random variable.

How to interpret this relation type

Record a genuine theorem implication that is not part of the target definition; these edges may point toward a weaker structure.

Relation sources

Independent random variablesLaw of large numbers

Permalink to relation

This is an authored directed relation from the source endpoint to the target endpoint.

Authored explanation

In the iid integrable case, the strong law gives almost-sure convergence of sample averages to the common mean; weaker variants use their own explicit assumptions.

How to interpret this relation type

Record a genuine theorem implication that is not part of the target definition; these edges may point toward a weaker structure.

Relation sources