Experimental Design
Bayesian Model Averaging (BMA) is a statistical technique used to account for model uncertainty by combining predictions from multiple models, weighted by their posterior probabilities. This method helps improve prediction accuracy and makes the results more robust by acknowledging that no single model can perfectly explain the data. BMA is particularly useful in experimental design as it allows researchers to integrate information from various models, enhancing decision-making processes and interpretations.
congrats on reading the definition of Bayesian Model Averaging. now let's actually learn it.