Bayesian logistic regression
E898980
Bayesian logistic regression is a probabilistic classification method that models binary outcomes using a logistic link function with prior distributions on the parameters, enabling full Bayesian inference and uncertainty quantification.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Bayesian logistic regression canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11002253 — 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 logistic regression Context triple: [Gibbs sampling, usedIn, Bayesian logistic regression]
-
A.
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.
-
B.
Bayesian model averaging
Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
-
C.
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.
-
D.
Bayes
Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
-
E.
Bayes factor
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
- 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 logistic regression Target entity description: Bayesian logistic regression is a probabilistic classification method that models binary outcomes using a logistic link function with prior distributions on the parameters, enabling full Bayesian inference and uncertainty quantification.
-
A.
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.
-
B.
Bayesian model averaging
Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
-
C.
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.
-
D.
Bayes
Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
-
E.
Bayes factor
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Bayesian model
ⓘ
classification method ⓘ generalized linear model ⓘ statistical model ⓘ |
| advantage |
automatic tradeoff between fit and complexity
ⓘ
coherent uncertainty estimates for parameters ⓘ coherent uncertainty estimates for predictions ⓘ incorporation of prior knowledge ⓘ |
| assumes | conditional independence of labels given features and parameters ⓘ |
| basedOn | logistic regression ⓘ |
| belongsTo |
Bayesian statistics
ⓘ
linked to:
Bayesian inference
machine learning ⓘ probabilistic modeling ⓘ |
| canIncorporate | feature selection via sparsity-inducing priors ⓘ |
| enables |
posterior predictive distributions
ⓘ
probabilistic classification ⓘ uncertainty quantification ⓘ |
| extends | frequentist logistic regression ⓘ |
| handles |
regularization via priors
ⓘ
small sample sizes ⓘ |
| implementedIn |
JAGS
ⓘ
PyMC ⓘ
linked to:
PyMC3
Stan ⓘ |
| inferenceMethod |
Hamiltonian Monte Carlo
ⓘ
Laplace approximation ⓘ Markov chain Monte Carlo ⓘ expectation propagation ⓘ variational inference ⓘ |
| likelihoodFunction |
Bernoulli likelihood
ⓘ
binomial likelihood ⓘ |
| models |
binary outcomes
ⓘ
probabilities of class membership ⓘ |
| output |
posterior distribution over coefficients
ⓘ
posterior predictive distribution over labels ⓘ |
| parameterSpace |
intercept term
ⓘ
regression coefficients ⓘ |
| relatedTo |
Bayesian generalized linear models
ⓘ
Bayesian probit regression ⓘ |
| typicalPrior |
Cauchy prior on coefficients
ⓘ
Gaussian prior on coefficients ⓘ Laplace prior on coefficients ⓘ hierarchical priors ⓘ |
| usedFor |
binary classification
ⓘ
credit scoring ⓘ medical diagnosis ⓘ risk prediction ⓘ text classification ⓘ |
| uses |
Bayesian inference
ⓘ
prior distributions on parameters ⓘ |
| usesLinkFunction | logistic link ⓘ |
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 logistic regression Description of subject: Bayesian logistic regression is a probabilistic classification method that models binary outcomes using a logistic link function with prior distributions on the parameters, enabling full Bayesian inference and uncertainty quantification.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.