Bayesian linear regression

(2 hours to learn)


By interpreting linear regression as a Bayesian model, we can automatically infer the prior variance and the noise variance, and make calibrated predictions. Bayesian linear regression is a useful component in fancier probabilistic models.


This concept has the prerequisites:


  • Know the form of the Bayesian linear regression model
  • Visualize the prior, evidence, and posterior
  • Derive the predictive distribution
  • Visualize the posterior predictive distribution
  • Be able to infer the variance parameters (with the evidence approximation or a conjugate prior)

Core resources (read/watch one of the following)


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


See also