What Are the Key Elements of Bayesian Adaptive Designs?
Several key elements define Bayesian adaptive designs:
1. Prior Distribution: This represents the initial beliefs about the parameters before any data are collected. 2. Likelihood Function: This describes the probability of the observed data given the parameters. 3. Posterior Distribution: This is the updated belief about the parameters after considering the observed data. 4. Decision Rules: Pre-specified criteria used to make real-time decisions based on the posterior distribution.
