Bayesian logistic regression

(1.8 hours to learn)


A Bayesian version of logistic regression.


This concept has the prerequisites:


  • Know the form of the Bayesian logistic regression model
  • Be able to estimate the parameters of the model computationally (e.g. with the Laplace approximation or EP)
  • Be able to approximate the predictive distribution computationally (e.g. with sampling or the probit approximation)

Core resources (read/watch one of the following)


Supplemental resources (the following are optional, but you may find them useful)


See also