Bayesian decision theory

(1 hours to learn)


When we use Bayesian parameter estimation techniques, often it's because we want to make a decision. In Bayesian decision theory, we make the choice which minimizes the expected loss under the posterior. When we compute a statistic like the mode or the mean of the predictive distribution, this can be interpreted as the decision theoretic solution under a particular loss function.


This concept has the prerequisites:


  • Know how the optimal decision is defined (in terms of minimizing expected loss with respect to the posterior)
  • Derive the form of the estimator for some particular loss functions:
    • 0-1 loss
    • quadratic loss
    • absolute loss

Core resources (read/watch one of the following)


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


See also