Bayesian logistic regression

(1.8 hours to learn)

Summary

A Bayesian version of logistic regression.

Context

This concept has the prerequisites:

Goals

  • 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)

-Paid-

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

-Paid-

See also