Pólya’s urn model
E637314
Pólya’s urn model is a classic probabilistic scheme in which drawing and then reinforcing the color of balls in an urn produces rich-get-richer dynamics and illustrates concepts like contagion, dependence, and random reinforcement.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Pólya urn scheme | 1 |
| Pólya’s urn model canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7030867 — 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: Pólya’s urn model Context triple: [George Pólya, notableIdea, Pólya’s urn model]
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A.
The Theory of Probability
The Theory of Probability is Hans Reichenbach’s influential philosophical and mathematical treatise that helped establish a rigorous, frequency-based interpretation of probability within the logical empiricist tradition.
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B.
The Twelvefold Way
The Twelvefold Way is a framework in combinatorics that systematically classifies twelve fundamental ways of counting functions between finite sets under various labeling and structural constraints.
-
C.
Khinchin–Pollaczek formula
The Khinchin–Pollaczek formula is a result in probability theory and queueing theory that provides an explicit expression for the stationary waiting-time distribution in certain single-server queues.
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D.
Erdős–Rényi law of large numbers
The Erdős–Rényi law of large numbers is a refinement of the classical law of large numbers that provides precise asymptotic behavior and convergence rates for sums of independent random variables, developed by mathematicians Pál Erdős and Alfréd Rényi.
-
E.
Foundations of the Theory of Probability
Foundations of the Theory of Probability is a landmark 1933 monograph that rigorously established modern probability theory on an axiomatic measure-theoretic basis.
- 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: Pólya’s urn model Target entity description: Pólya’s urn model is a classic probabilistic scheme in which drawing and then reinforcing the color of balls in an urn produces rich-get-richer dynamics and illustrates concepts like contagion, dependence, and random reinforcement.
-
A.
The Theory of Probability
The Theory of Probability is Hans Reichenbach’s influential philosophical and mathematical treatise that helped establish a rigorous, frequency-based interpretation of probability within the logical empiricist tradition.
-
B.
The Twelvefold Way
The Twelvefold Way is a framework in combinatorics that systematically classifies twelve fundamental ways of counting functions between finite sets under various labeling and structural constraints.
-
C.
Khinchin–Pollaczek formula
The Khinchin–Pollaczek formula is a result in probability theory and queueing theory that provides an explicit expression for the stationary waiting-time distribution in certain single-server queues.
-
D.
Erdős–Rényi law of large numbers
The Erdős–Rényi law of large numbers is a refinement of the classical law of large numbers that provides precise asymptotic behavior and convergence rates for sums of independent random variables, developed by mathematicians Pál Erdős and Alfréd Rényi.
-
E.
Foundations of the Theory of Probability
Foundations of the Theory of Probability is a landmark 1933 monograph that rigorously established modern probability theory on an axiomatic measure-theoretic basis.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
probabilistic model
ⓘ
reinforcement process ⓘ urn model ⓘ |
| coreMechanism |
random reinforcement
ⓘ
rich-get-richer dynamics ⓘ sampling without replacement with reinforcement ⓘ |
| field |
probability theory
ⓘ
random processes ⓘ statistics ⓘ stochastic processes ⓘ |
| generalizationOf | simple urn sampling without reinforcement ⓘ |
| hasComponent |
colored balls
ⓘ
urn ⓘ |
| hasProperty |
exhibits contagion-like behavior
ⓘ
exhibits rich-get-richer behavior ⓘ limit proportion is random ⓘ martingale structure for color proportions ⓘ non-Markovian in colors alone ⓘ non-independent draws ⓘ path-dependent distribution ⓘ reinforcement of drawn color ⓘ symmetry between colors in the classical version ⓘ |
| hasVariant |
balanced Pólya urn
NERFINISHED
ⓘ
generalized Pólya urn ⓘ multi-color Pólya urn NERFINISHED ⓘ unbalanced Pólya urn ⓘ |
| illustratesConcept |
contagion
ⓘ
dependence ⓘ exchangeability ⓘ path dependence ⓘ random limit proportions ⓘ reinforcement learning intuition ⓘ self-reinforcing processes ⓘ |
| namedAfter | George Pólya NERFINISHED ⓘ |
| relatedTo |
Chinese restaurant process
NERFINISHED
ⓘ
Dirichlet distribution NERFINISHED ⓘ Dirichlet-multinomial distribution NERFINISHED ⓘ beta distribution ⓘ preferential attachment models ⓘ |
| timeEvolution | discrete time ⓘ |
| typicalInitialCondition | finite number of balls of each color ⓘ |
| updateRule |
add additional balls of the same color
ⓘ
draw a ball at random ⓘ observe its color ⓘ return the ball to the urn ⓘ |
| usedIn |
Bayesian statistics
NERFINISHED
ⓘ
Dirichlet process constructions NERFINISHED ⓘ epidemic modeling intuition ⓘ modeling contagion processes ⓘ network growth models ⓘ preferential attachment analogies ⓘ reinforcement learning intuition ⓘ |
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: Pólya’s urn model Description of subject: Pólya’s urn model is a classic probabilistic scheme in which drawing and then reinforcing the color of balls in an urn produces rich-get-richer dynamics and illustrates concepts like contagion, dependence, and random reinforcement.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Pólya urn scheme