Probability theory and stochastic processes Foundations: probability spaces and events Concept
Cumulative distribution function
Summary
The cumulative distribution function of a real random variable gives, for each real , the probability that is at most . It determines the law of . The node cumulative distribution function gives the precise definition.
Where this leads
13 pages build on this one.
Further reading
About this page
| Tag | 0H8H7ZTHW2 |
|---|---|
| URI | m2://0H8H7ZTHW2 |
| Aliases | cdf, distribution-function |
| Kind | concept |
| Writing | W1 stated |
| MSC | 60E05 |
| Same as |
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What this page needs for W2
- edge instance_of in: at least two examples (0 of 2)
- edge not_instance_of in: at least one non-example (0 of 1)
The writing levels say how complete the text of a page is. They do not measure its truth.
What links here
Definitions
Used by
- Inverse transform sampling
- Pareto distribution
- Copula
- Asymptotic behavior of solutions of renewal equations
- Glivenko–Cantelli theorem
- Probit model
- Berry–Esseen theorem
- Survival function
- Stochastic ordering
- Empirical process
- Fisher–Tippett–Gnedenko theorem
- Sklar's theorem