Bayesian epistemology
E679748
application of Bayesian probability
approach in formal epistemology
epistemological theory
theory of rational belief
Bayesian epistemology is a theory of knowledge that models rational belief and updating in terms of subjective probabilities governed by the rules of Bayesian probability theory.
All labels observed (4)
| Label | Occurrences |
|---|---|
| Bayesian confirmation theory | 3 |
| Bayesian epistemology canonical | 3 |
| Bayesian conditionalization | 1 |
| Bayesian reasoning | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7657936 — 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: Bayesian epistemology Context triple: [Observational selection effects and probability, mainSubject, Bayesian epistemology]
-
A.
Epistemic Justification
Epistemic Justification is a work in philosophy that examines how and when beliefs are rationally supported by evidence and reasoning.
-
B.
Truth and Probability
Truth and Probability is a foundational 1926 essay by philosopher F. P. Ramsey that develops a subjective theory of probability and lays groundwork for modern Bayesian decision theory.
-
C.
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.
-
D.
Of Knowledge and Probability
"Of Knowledge and Probability" is a section in John Locke’s *An Essay Concerning Human Understanding* that analyzes the nature, degrees, and limits of human knowledge in contrast with mere probability or belief.
-
E.
Epistemology Naturalized
Epistemology Naturalized is W.V.O. Quine’s influential proposal to reconceive traditional epistemology as a branch of empirical psychology, focusing on how humans actually form beliefs rather than on a priori justification.
- 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: Bayesian epistemology Target entity description: Bayesian epistemology is a theory of knowledge that models rational belief and updating in terms of subjective probabilities governed by the rules of Bayesian probability theory.
-
A.
Epistemic Justification
Epistemic Justification is a work in philosophy that examines how and when beliefs are rationally supported by evidence and reasoning.
-
B.
Truth and Probability
Truth and Probability is a foundational 1926 essay by philosopher F. P. Ramsey that develops a subjective theory of probability and lays groundwork for modern Bayesian decision theory.
-
C.
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.
-
D.
Of Knowledge and Probability
"Of Knowledge and Probability" is a section in John Locke’s *An Essay Concerning Human Understanding* that analyzes the nature, degrees, and limits of human knowledge in contrast with mere probability or belief.
-
E.
Epistemology Naturalized
Epistemology Naturalized is W.V.O. Quine’s influential proposal to reconceive traditional epistemology as a branch of empirical psychology, focusing on how humans actually form beliefs rather than on a priori justification.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
application of Bayesian probability
ⓘ
approach in formal epistemology ⓘ epistemological theory ⓘ theory of rational belief ⓘ |
| appliedIn |
artificial intelligence
ⓘ
cognitive science ⓘ confirmation theory ⓘ philosophy of science ⓘ statistics ⓘ |
| associatedWith |
Bayesian confirmation theory
NERFINISHED
ⓘ
Bayesian decision theory NERFINISHED ⓘ Bayesian learning ⓘ Bayesian networks NERFINISHED ⓘ objective Bayesianism ⓘ subjective Bayesianism ⓘ |
| basedOn |
Dutch book arguments
ⓘ
probability calculus ⓘ representation theorems ⓘ |
| characterizes |
learning as conditionalization
ⓘ
rational agents as having probabilistic credences ⓘ rationality in terms of coherence constraints ⓘ |
| contrastsWith |
Popperian falsificationism
ⓘ
classical foundationalism ⓘ evidentialism without probabilities ⓘ reliabilist epistemology ⓘ |
| fieldOfStudy |
epistemology
ⓘ
philosophy of science ⓘ |
| focusesOn |
belief updating
ⓘ
coherence of beliefs ⓘ confirmation theory ⓘ degrees of belief ⓘ evidential support ⓘ rational belief ⓘ rational decision under uncertainty ⓘ |
| hasKeyConcept |
Dutch book coherence
ⓘ
coherence ⓘ conditionalization ⓘ credence ⓘ likelihood ⓘ posterior probability ⓘ prior probability ⓘ |
| hasKeyQuestion |
how evidence confirms or disconfirms hypotheses
ⓘ
how should agents update on new evidence ⓘ how should rational agents assign prior probabilities ⓘ |
| models |
belief as credence
ⓘ
evidence as information that changes probabilities ⓘ inductive inference ⓘ updating by conditionalization on evidence ⓘ |
| uses |
Bayesian probability theory
NERFINISHED
ⓘ
Bayes’ theorem NERFINISHED ⓘ conditional probability ⓘ subjective probability ⓘ |
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: Bayesian epistemology Description of subject: Bayesian epistemology is a theory of knowledge that models rational belief and updating in terms of subjective probabilities governed by the rules of Bayesian probability theory.
Referenced by (8)
Full triples — surface form annotated when it differs from this entity's canonical label.
Rationality: What It Is, Why It Seems Scarce, Why It Matters
→
mainSubject
→
Bayesian epistemology
ⓘ
this entity surface form:
Bayesian reasoning
this entity surface form:
Bayesian confirmation theory
this entity surface form:
Bayesian conditionalization
this entity surface form:
Bayesian confirmation theory
subject surface form:
Ravens paradox
this entity surface form:
Bayesian confirmation theory
subject surface form:
Ravens paradox
subject surface form:
Truth and Probability