Bayesian adaptive designs operate on the principle of updating prior beliefs with new evidence. Initially, researchers specify a prior distribution based on existing knowledge or expert opinion. As the study progresses and data are collected, the prior distribution is updated to a posterior distribution using Bayes' theorem. This posterior distribution then informs decisions about the study, such as whether to continue, stop, or modify the trial.
