Bayesian Decision Theory is a statistical approach that helps in making decisions under uncertainty. It combines prior knowledge with new evidence to update beliefs about the likelihood of different outcomes. This is done using Bayes' Theorem, which calculates the probability of a hypothesis based on prior probabilities and the likelihood of observed data. In practice, Bayesian Decision Theory involves defining possible actions, their consequences, and the probabilities of different states of the world. By evaluating the expected utility of each action, decision-makers can choose the option that maximizes their expected benefit, leading to more informed and rational choices.