Bayesian logistic regression analysis | Zendy

N. van Erp | Zendy; Pieter van Gelder | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Bayesian logistic regression analysis

Author(s) -

N. van Erp,

Pieter van Gelder

Publication year - 2013

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.4819994

Subject(s) - logistic regression , posterior probability , bayesian linear regression , statistics , mathematics , bayesian probability , event (particle physics) , bayes' theorem , prior probability , regression analysis , jacobian matrix and determinant , econometrics , computer science , bayesian inference , physics , quantum mechanics

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an essential added ingredient. The application of the product rule gives the posterior of the unknown logistic regression coefficients. The Jacobian transformation then maps the posterior of these regression coefficients to the posterior of the corresponding probability of some event and some nuisance parameters. Finally, by way of the sumrule the nuissance parameters are integrated out

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research