Bayesian logistic regression analysis

Conference paper (2012)
Department
Hydraulic Engineering (CEG) (TU Delft)
Copyright: © 2013 AIP Publishing LLC
DOI: https://doi.org/doi:10.1063/1.4819994
Regression Logistic regression
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Published Date

15-07-2012

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Source:

AIP Conference Proceedings 1553. Bayesian Conference and Maximum Entropy Methods in Science and Engineering: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Garching, Germany, 15-20 July 2012

ISBN:

978-0-7354-1179-1

Source:

11650015-b42c-4fae-a1d2-437ffbeeb011

Faculty

Civil Engineering and Geosciences

Department

Hydraulic Engineering

Abstract

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.

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