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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| double exponential distribution canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T6939200 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: double exponential distribution Context triple: [Laplace law of error, alsoKnownAs, double exponential distribution]
-
A.
Pareto distribution
The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
-
B.
Tukey's lambda distribution
Tukey's lambda distribution is a flexible family of probability distributions used primarily for exploratory data analysis and modeling diverse shapes of data, including varying degrees of skewness and kurtosis.
-
C.
Exponent
Exponent is a software development company best known for creating Expo, a popular framework and toolchain for building React Native applications.
-
D.
Gumbel
Gumbel is a surname most notably associated with American sportscaster Greg Gumbel.
-
E.
Poisson
Poisson is a French surname most famously associated with Siméon Denis Poisson, a prominent 19th-century mathematician and physicist known for major contributions to probability theory and mathematical physics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: double exponential distribution Target entity description: 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.
-
A.
Pareto distribution
The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
-
B.
Tukey's lambda distribution
Tukey's lambda distribution is a flexible family of probability distributions used primarily for exploratory data analysis and modeling diverse shapes of data, including varying degrees of skewness and kurtosis.
-
C.
Exponent
Exponent is a software development company best known for creating Expo, a popular framework and toolchain for building React Native applications.
-
D.
Gumbel
Gumbel is a surname most notably associated with American sportscaster Greg Gumbel.
-
E.
Poisson
Poisson is a French surname most famously associated with Siméon Denis Poisson, a prominent 19th-century mathematician and physicist known for major contributions to probability theory and mathematical physics.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf | probability distribution ⓘ |
| alsoKnownAs |
Laplace distribution
ⓘ
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 ⓘ |
| 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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: double exponential distribution Description of subject: 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.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.