Probability theory and stochastic processes Expectation, moments and transforms Concept
Variance
Summary
The variance of a random variable is the expected squared distance between the variable and its mean. It measures how widely the values spread around the mean. The node variance gives the precise definition.
Where this leads
25 pages build on this one.
- Cramér–Rao bound
- Efficiency of an estimator
- Koksma–Hlawka inequality
- Heteroscedasticity
- Importance sampling
- Variogram
- Multilevel Monte Carlo method
- Kolmogorov's inequality
- Monte Carlo integration
- Lindeberg's condition
- Berry–Esseen theorem
- Fisher information
- Variance reduction
- Bernstein inequalities
- Gauss–Markov theorem
- Linear regression
- Lehmann–Scheffé theorem
- Principal component analysis
- Law of the iterated logarithm
- Kolmogorov's three-series theorem
- Errors-in-variables model
- Control variates
- Antithetic variates
- Generalized least squares
- Propagation of uncertainty
Further reading
- Web https://en.wikipedia.org/wiki/Variance, lead section
About this page
| Tag | 0477R9PYAE |
|---|---|
| URI | m2://0477R9PYAE |
| Aliases | variance-of-random-variable, second-central-moment |
| Kind | concept |
| Writing | W1 stated |
| MSC | 60A05 |
| 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)
- edge specializes both: the more general notions and the special cases (0 of 1)
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What links here
Definitions
Used by
- Cramér–Rao bound
- Heteroscedasticity
- Importance sampling
- Variogram
- Multilevel Monte Carlo method
- Kolmogorov's inequality
- Monte Carlo integration
- Lindeberg's condition
- Berry–Esseen theorem
- Fisher information
- Variance reduction
- Bernstein inequalities
- Gauss–Markov theorem
- Linear regression
- Lehmann–Scheffé theorem
- Principal component analysis
- Law of the iterated logarithm
- Kolmogorov's three-series theorem
- Errors-in-variables model
- Control variates
- Antithetic variates
- Propagation of uncertainty