Bayesian decision theory provides a mathematical framework for making optimal decisions under uncertainty. It is based on Bayesian probability, which updates the probability of a hypothesis as more evidence becomes available.
Bayesian decision theory formally incorporates prior knowledge and uncertainties into a mathematical optimization framework. It provides an approach to sequential decision-making under uncertainty that is rational, consistent, and updatable as new evidence arrives. Applications range from clinical trials to spam filtering and beyond.
In this session, we will cover the formulation of Bayesian Decision Theory, together with the related terminology and methods, such as discriminant function, decision boundaries, etc..