In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. They arise particularly in the use of conjugate priors.
| Property | Value |
|---|
| dbpprop:abstract
|
- In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. They arise particularly in the use of conjugate priors. For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution, then: p is a parameter of the underlying system (Bernoulli distribution), and α and β are parameters of the prior distribution (beta distribution), hence hyperparameters. One may take a single value for a given hyperparameter, or one can iterate and take a probability distribution on the hyperparameter itself, called a hyperprior.
|
| dbpprop:hasPhotoCollection
| |
| dbpprop:reference
| |
| rdfs:comment
|
- In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. They arise particularly in the use of conjugate priors.
|
| rdfs:label
| |
| owl:sameAs
| |
| skos:subject
| |
| foaf:page
| |
| is dbpprop:redirect
of | |
![[RDF Data]](/statics/sw-cube.png)
