Pearson distribution
E665238
The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Pearson distribution canonical | 1 |
| Pearson type VII distribution | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7454019 — 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: Pearson distribution Context triple: [Karl Pearson, notableWork, Pearson distribution]
-
A.
Carsey-Werner Distribution
Carsey-Werner Distribution is a television distribution company best known for handling popular sitcoms produced by Carsey-Werner, including major hits from the late 1980s and 1990s.
-
B.
Dirichlet distribution
The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
-
C.
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.
-
D.
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.
-
E.
Laplace distribution
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.
- 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: Pearson distribution Target entity description: The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
-
A.
Carsey-Werner Distribution
Carsey-Werner Distribution is a television distribution company best known for handling popular sitcoms produced by Carsey-Werner, including major hits from the late 1980s and 1990s.
-
B.
Dirichlet distribution
The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
-
C.
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.
-
D.
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.
-
E.
Laplace distribution
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.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
continuous probability distribution
ⓘ
probability distribution family ⓘ statistical model ⓘ |
| appliesTo | univariate continuous random variables ⓘ |
| assumes | continuous support on an interval ⓘ |
| belongsTo | Pearson system of distributions NERFINISHED ⓘ |
| canBeEstimatedBy |
maximum likelihood estimation
ⓘ
method of moments ⓘ |
| characterizedBy |
kurtosis
ⓘ
mean ⓘ skewness ⓘ variance ⓘ |
| definedBy | first-order differential equation for the density ⓘ |
| field |
probability theory
ⓘ
statistics ⓘ |
| generalizes |
Student t-distribution
NERFINISHED
ⓘ
beta distribution ⓘ gamma distribution NERFINISHED ⓘ normal distribution ⓘ |
| hasAlternativeName | Pearson system NERFINISHED ⓘ |
| hasGoal | to model data with given skewness and kurtosis ⓘ |
| hasParameter |
location parameter
ⓘ
scale parameter ⓘ shape parameter ⓘ |
| hasProperty | can approximate many common distributions ⓘ |
| hasSubclass |
Pearson Type I distribution
NERFINISHED
ⓘ
Pearson Type II distribution NERFINISHED ⓘ Pearson Type III distribution NERFINISHED ⓘ Pearson Type IV distribution NERFINISHED ⓘ Pearson Type V distribution NERFINISHED ⓘ Pearson Type VI distribution ⓘ Pearson Type VII distribution NERFINISHED ⓘ |
| hasTypeClassification | Type I–Type VII ⓘ |
| introducedBy | Karl Pearson NERFINISHED ⓘ |
| introducedInYear | 1895 ⓘ |
| namedAfter | Karl Pearson NERFINISHED ⓘ |
| supports |
flexible kurtosis
ⓘ
flexible skewness ⓘ |
| usedFor |
approximating unknown distributions
ⓘ
fitting empirical distributions ⓘ modeling kurtotic data ⓘ modeling skewed data ⓘ |
| usedIn |
biostatistics
ⓘ
finance ⓘ hydrology ⓘ quality control ⓘ reliability engineering ⓘ |
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: Pearson distribution Description of subject: The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Pearson type VII distribution