Probability theory and stochastic processes Definition
Normal distribution
Builds on Probability density functionDirac measureDensity of the normal distribution
Definition
Let and . The normal distribution with mean and variance , written , is the distribution on with the probability density function The standard normal distribution is .
Remarks
The function is the density of the normal distribution. Some authors, and the library Mathlib, also allow . Then is the Dirac measure . If and , are real, then . In particular, . The cumulative distribution function of is written . It has no formula with elementary functions.
Reading path
To understand this page from the ground up, read these 46 pages in order. The foundations come first.
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Complement definition
- Function definition
- Interval definition
- Indicator function definition
- Preimage of a set definition
- Sigma-algebra definition
- Event definition
- Borel sigma-algebra definition
- Probability axioms concept
- Probability measure definition
- Probability distribution definition
- Dirac measure definition
- Probability space definition
- Random variable definition
- Cumulative distribution function definition
- Random variable with a density definition
- Probability density function definition
- Density of the normal distribution definition
- Normal distribution definition
Where this leads
44 pages build on this one.
- Variance of the normal distribution
- De Moivre–Laplace theorem
- Mean of the normal distribution
- Moment-generating function of the normal distribution
- Uncorrelated random variables that are not independent
- Kurtosis
- Chi-squared test
- Central limit theorem
- 68–95–99.7 rule
- Multivariate normal distribution
- Student's t-test
- Confidence interval
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Chi-squared distribution
- Bernstein–von Mises theorem
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Contiguity of measures
- Lindley's paradox
- Score test
- Asymptotic normality of the maximum likelihood estimator
- F-distribution
- Edgeworth expansion
- Erdős–Kac theorem
- Monte Carlo integration
- Delta method
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Behrens–Fisher problem
- Kruskal–Wallis test
- Wishart distribution
- Empirical likelihood
- Berry–Esseen theorem
- Cochran's theorem
- Independence of sample mean and sample variance for normal samples
- Hotelling's T-squared distribution
- Wald test
- Binomial proportion confidence interval
- Neyman construction
- Wilks' theorem
- Local asymptotic normality
- Frequentist inference
Further reading
- Web https://en.wikipedia.org/wiki/Normal_distribution, General normal distribution
About this page
| Tag | 07RXBVTVZB |
|---|---|
| URI | m2://07RXBVTVZB |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60E05 |
| Defines | Normal distribution |
| Uses | Probability density function, Dirac measure, Density of the normal distribution |
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What this page needs for W2
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What links here
Used by
- Variance of the normal distribution
- De Moivre–Laplace theorem
- Mean of the normal distribution
- Moment-generating function of the normal distribution
- Uncorrelated random variables that are not independent
- Kurtosis
- Central limit theorem
- 68–95–99.7 rule
- Multivariate normal distribution
- Student's t-test
- Confidence interval
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Chi-squared distribution
- Hodges' superefficient estimator
- Contiguity of measures
- Lindley's paradox
- Edgeworth expansion
- Delta method
- Noncentral chi-squared distribution
- Behrens–Fisher problem
- Independence of sample mean and sample variance for normal samples