Gaussian distribution

E29361

The Gaussian distribution, also known as the normal distribution, is a fundamental continuous probability distribution characterized by its symmetric bell-shaped curve and central role in statistics and the natural sciences.


Statements (50)
Predicate Object
instanceOf Gaussian distribution
continuous probability distribution
normal distribution
probability distribution
appliesTo measurement errors
sum of many independent random variables
cumulativeDistributionFunction Φ((x−μ)/σ)
definedOn real numbers
hasAlias Gaussian
bell curve
normal distribution
hasCharacteristicFunction φ(t) = exp(iμt − ½σ²t²)
hasExcessKurtosis 0
hasInflectionPointsAt μ + σ
μ − σ
hasMean 0
hasMeanSymbol μ
hasMomentGeneratingFunction M(t) = exp(μt + ½σ²t²)
hasProperty bell-shaped
symmetric
unimodal
hasSkewness 0
hasSpecialCase standard normal distribution
hasStandardDeviationSymbol σ
hasVariance 1
hasVarianceSymbol σ²
isFullyDeterminedBy its mean and variance
isLimitIn central limit theorem
isMaximumEntropyDistributionGiven fixed mean and variance
isSymmetricAbout its mean
medianEquals mean
modeEquals mean
originatesFrom work of Carl Friedrich Gauss
parameter location parameter
mean
scale parameter
standard deviation
variance
probabilityDensityFunction f(x) = (1/(σ√(2π))) · exp(−(x−μ)²/(2σ²))
support (−∞, +∞)
usedIn Bayesian inference
engineering
error analysis
finance
hypothesis testing
machine learning
natural sciences
regression analysis
signal processing
statistics

Referenced by (5)

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