Print Email Facebook Twitter Bayesian logistic regression analysis Title Bayesian logistic regression analysis Author Van Erp, H.R.N. Van Gelder, P.H.A.J.M. Faculty Civil Engineering and Geosciences Department Hydraulic Engineering Date 2012-07-15 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. Subject regressionlogistic regression To reference this document use: http://resolver.tudelft.nl/uuid:11650015-b42c-4fae-a1d2-437ffbeeb011 DOI https://doi.org/10.1063/1.4819994 Publisher American Institute of Physics ISBN 978-0-7354-1179-1 Source https://doi.org/10.1063/1.4819994 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 Part of collection Institutional Repository Document type conference paper Rights © 2013 AIP Publishing LLC Files PDF vanErp_2013.pdf 129.92 KB Close viewer /islandora/object/uuid:11650015-b42c-4fae-a1d2-437ffbeeb011/datastream/OBJ/view