Foundations of the Theory of Probability
E320430
Foundations of the Theory of Probability is a landmark 1933 monograph that rigorously established modern probability theory on an axiomatic measure-theoretic basis.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Foundations of the Theory of Probability canonical | 2 |
| Grundbegriffe der Wahrscheinlichkeitsrechnung | 1 |
| The Theory of Probability | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3037533 — 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: Foundations of the Theory of Probability Context triple: [Andrei Kolmogorov, notableWork, Foundations of the Theory of Probability]
-
A.
Théorie analytique des probabilités
Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
-
B.
A Treatise on Probability
A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
-
C.
Modern Probability Theory and Its Applications
"Modern Probability Theory and Its Applications" is a foundational textbook by Emanuel Parzen that systematically develops modern probability theory and demonstrates its use in a wide range of statistical and applied contexts.
-
D.
The Logic of Chance
The Logic of Chance is an influential 1866 book by John Venn that helped establish the frequency interpretation of probability and advanced the philosophical foundations of statistical reasoning.
-
E.
Logical Foundations of Probability
Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
- 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: Foundations of the Theory of Probability Target entity description: Foundations of the Theory of Probability is a landmark 1933 monograph that rigorously established modern probability theory on an axiomatic measure-theoretic basis.
-
A.
Théorie analytique des probabilités
Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
-
B.
A Treatise on Probability
A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
-
C.
Modern Probability Theory and Its Applications
"Modern Probability Theory and Its Applications" is a foundational textbook by Emanuel Parzen that systematically develops modern probability theory and demonstrates its use in a wide range of statistical and applied contexts.
-
D.
The Logic of Chance
The Logic of Chance is an influential 1866 book by John Venn that helped establish the frequency interpretation of probability and advanced the philosophical foundations of statistical reasoning.
-
E.
Logical Foundations of Probability
Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
mathematics book ⓘ monograph ⓘ |
| author |
Andrei Kolmogorov
ⓘ
surface form:
Andrey Kolmogorov
|
| basedOn | measure theory ⓘ |
| countryOfOrigin | Soviet Union ⓘ |
| defines |
conditional probability
ⓘ
distribution function ⓘ expectation as Lebesgue integral ⓘ independence of events ⓘ random variable as measurable function ⓘ |
| describedAs |
foundational text of modern probability
ⓘ
landmark work in probability theory ⓘ |
| establishes |
Kolmogorov axioms as standard framework
ⓘ
measure-theoretic basis of probability ⓘ |
| field |
mathematics
ⓘ
measure theory ⓘ probability theory ⓘ |
| hasEdition |
English edition
ⓘ
German edition ⓘ |
| hasImpactOn |
formalization of stochastic processes
ⓘ
modern textbooks in probability theory ⓘ rigorous treatment of random variables ⓘ |
| influenced |
ergodic theory
ⓘ
mathematical statistics ⓘ modern probability theory ⓘ stochastic processes ⓘ |
| influencedBy |
Lebesgue integration
ⓘ
measure theory of Henri Lebesgue ⓘ measure theory of Émile Borel ⓘ |
| introduces |
Kolmogorov axioms
ⓘ
axiomatic foundation of probability ⓘ probability measure ⓘ probability space ⓘ sample space ⓘ sigma-algebra ⓘ |
| languageOfEnglishEdition | English ⓘ |
| languageOfGermanEdition | German ⓘ |
| originalLanguage | Russian ⓘ |
| originalTitle |
Foundations of the Theory of Probability
self-linksurface differs
ⓘ
surface form:
Grundbegriffe der Wahrscheinlichkeitsrechnung
|
| publicationYear | 1933 ⓘ |
| publisherOfGermanEdition | Springer ⓘ |
| topic |
axiomatization of probability
ⓘ
conditional expectation ⓘ countable additivity ⓘ independent random variables ⓘ law of large numbers ⓘ limit theorems ⓘ |
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: Foundations of the Theory of Probability Description of subject: Foundations of the Theory of Probability is a landmark 1933 monograph that rigorously established modern probability theory on an axiomatic measure-theoretic basis.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
The Theory of Probability
Foundations of the Theory of Probability
→
originalTitle
→
Foundations of the Theory of Probability
self-linksurface differs
ⓘ
this entity surface form:
Grundbegriffe der Wahrscheinlichkeitsrechnung