double exponential distribution

E628629

The double exponential distribution, also known as the Laplace distribution, is a continuous probability distribution with a sharp peak at its mean and heavier tails than the normal distribution, often used to model data with abrupt changes or outliers.

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Statements (47)

Predicate Object
instanceOf probability distribution
alsoKnownAs Laplace distribution NERFINISHED
two-sided exponential distribution
belongsToFamily exponential family
location-scale family
canBeRepresentedAs scale mixture of normals with exponential mixing distribution
hasCharacteristicFunction φ(t) = exp(i μ t) / (1 + b^2 t^2)
hasCumulativeDistributionFunction F(x) = 0.5 exp((x-μ)/b) for x < μ
F(x) = 1 - 0.5 exp(-(x-μ)/b) for x ≥ μ
hasEntropy 1 + ln(2b)
hasExcessKurtosis 3
hasFiniteMomentsOfAllOrders true
hasFisherInformationForLocation 1/b^2
hasFisherInformationForScale 2/b^2
hasHazardFunction h(x) = f(x) / S(x)
hasHeavierTailsThan normal distribution
hasKurtosis 6
hasLocationParameter μ
hasLogConcaveDensity true
hasMean μ
hasMedian μ
hasMode μ
hasMomentGeneratingFunction M(t) = exp(μ t) / (1 - b^2 t^2), |t| < 1/b
hasProbabilityDensityFunction f(x|μ,b) = (1/(2b)) exp(-|x-μ|/b)
f(x|μ,λ) = (λ/2) exp(-λ|x-μ|)
hasRateParameterization b = 1/λ
hasScaleParameter b
hasSharperPeakThan normal distribution
hasSkewness 0
hasStandardDeviation √2 b
hasSupport (-∞, ∞)
hasSurvivalFunction S(x) = 1 - F(x)
hasVariance 2 b^2
isInfinitelyDivisible true
isLimitOf difference of two independent exponential variables with same rate
isNamedAfter Pierre-Simon Laplace NERFINISHED
isSubexponential false
isSymmetricAbout μ
isUsedFor Bayesian L1 regularization priors
modeling data with heavy tails
modeling data with sharp peaks
modeling outliers
robust regression error modeling
isUsedIn econometrics
image processing
signal processing
sparse Bayesian learning

Referenced by (1)

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Laplace law of error alsoKnownAs double exponential distribution