Modern Probability Theory and Its Applications
E274129
"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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Modern Probability Theory and Its Applications canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2515036 — 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: Modern Probability Theory and Its Applications Context triple: [Emanuel Parzen, hasPublication, Modern Probability Theory and Its Applications]
-
A.
Foundations of Probability
Foundations of Probability is a seminal textbook by mathematician Alfréd Rényi that presents a rigorous, axiomatic treatment of probability theory.
-
B.
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.
-
C.
Probability Theory
Probability Theory is a foundational branch of mathematics that studies random phenomena and quantifies uncertainty using concepts such as probability measures, random variables, and distributions.
-
D.
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.
-
E.
Wahrscheinlichkeitslehre
Wahrscheinlichkeitslehre is a foundational work in the philosophy and axiomatization of probability theory by Hans Reichenbach, influential in both mathematics and logical empiricism.
- 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: Modern Probability Theory and Its Applications Target entity description: "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.
-
A.
Foundations of Probability
Foundations of Probability is a seminal textbook by mathematician Alfréd Rényi that presents a rigorous, axiomatic treatment of probability theory.
-
B.
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.
-
C.
Probability Theory
Probability Theory is a foundational branch of mathematics that studies random phenomena and quantifies uncertainty using concepts such as probability measures, random variables, and distributions.
-
D.
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.
-
E.
Wahrscheinlichkeitslehre
Wahrscheinlichkeitslehre is a foundational work in the philosophy and axiomatization of probability theory by Hans Reichenbach, influential in both mathematics and logical empiricism.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
mathematics book
ⓘ
probability theory textbook ⓘ textbook ⓘ |
| author | Emanuel Parzen ⓘ |
| countryOfPublication |
United States
ⓘ
linked to:
United States of America
|
| describedAs |
foundational text for graduate-level probability
ⓘ
systematic development of modern probability theory ⓘ |
| field |
probability theory
ⓘ
statistics ⓘ |
| format | hardcover ⓘ |
| hasInfluenceOn | development of modern probability curricula ⓘ |
| hasPart |
chapters on applications to statistics
ⓘ
chapters on continuous probability ⓘ chapters on discrete probability ⓘ chapters on limit theorems ⓘ chapters on stochastic processes ⓘ |
| language | English ⓘ |
| notableFor |
integration of theory and applications in probability
ⓘ
rigorous treatment of probability with applications ⓘ |
| publicationYear | 1960 ⓘ |
| publisher | John Wiley & Sons ⓘ |
| subject |
Gaussian processes
ⓘ
Markov chains ⓘ
linked to:
Markov processes
Poisson processes ⓘ applications of probability in statistics ⓘ applied probability ⓘ central limit theorem ⓘ conditional expectation ⓘ distribution functions ⓘ ergodic theory (probabilistic aspects) ⓘ law of large numbers ⓘ limit theorems ⓘ martingales ⓘ measure-theoretic probability ⓘ modern probability theory ⓘ random variables ⓘ spectral analysis of time series ⓘ stationary processes ⓘ stochastic processes ⓘ |
| targetAudience |
applied scientists using probability
ⓘ
graduate students in mathematics ⓘ graduate students in statistics ⓘ |
| timePeriod | 20th century mathematics literature ⓘ |
| usedAs | university course textbook ⓘ |
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: Modern Probability Theory and Its Applications Description of subject: "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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.