Bayes factor
E700155
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Bayes factor canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7906458 — 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: Bayes factor Context triple: [Bayesian Occam factor, relatedTo, Bayes factor]
-
A.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
-
B.
Bayes’ theorem
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
-
C.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
-
D.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
E.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
- 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: Bayes factor Target entity description: The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
-
A.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
-
B.
Bayes’ theorem
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
-
C.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
-
D.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
E.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Bayesian model comparison metric
ⓘ
likelihood ratio ⓘ statistical measure ⓘ |
| alternativeName | Bayesian evidence ratio NERFINISHED ⓘ |
| appliedIn |
biostatistics
ⓘ
econometrics ⓘ machine learning ⓘ physics ⓘ psychology ⓘ |
| approximatedBy | BIC difference ⓘ |
| compares |
two hypotheses
ⓘ
two statistical models ⓘ |
| computedBy |
Laplace approximation
NERFINISHED
ⓘ
Monte Carlo methods ⓘ bridge sampling ⓘ reversible jump MCMC ⓘ |
| contrastsWith |
frequentist hypothesis tests
ⓘ
p-values ⓘ |
| definedAs | ratio of marginal likelihoods of two models ⓘ |
| dependsOn |
likelihood function
ⓘ
prior distributions ⓘ |
| domain | statistical inference ⓘ |
| hasFormula | B_10 = p(y | M_1) / p(y | M_0) ⓘ |
| hasProperty |
can accumulate evidence for the null model
ⓘ
can be computed analytically in simple models ⓘ often requires numerical integration ⓘ penalizes model complexity via marginalization ⓘ sensitive to prior specification ⓘ |
| interpretedAs | how much data update prior odds to posterior odds ⓘ |
| interpretedUsing |
Jeffreys scale
NERFINISHED
ⓘ
Kass and Raftery scale NERFINISHED ⓘ |
| introducedBy | Harold Jeffreys NERFINISHED ⓘ |
| invariantUnder | reparameterization of model parameters ⓘ |
| measures | strength of evidence in data for one model over another ⓘ |
| quantifies | relative evidence from data ⓘ |
| relatedTo |
Bayesian Information Criterion
NERFINISHED
ⓘ
marginal likelihood ⓘ posterior odds ⓘ prior odds ⓘ |
| symbol |
B_10
ⓘ
K ⓘ |
| usedFor |
hypothesis testing
ⓘ
model comparison ⓘ |
| usedIn | Bayesian statistics NERFINISHED ⓘ |
| usedTo |
assess evidence for alternative hypothesis
ⓘ
assess evidence for null hypothesis ⓘ select between nested models ⓘ select between non-nested models ⓘ |
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: Bayes factor Description of subject: The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.