Bayesian model averaging

Bayesian model averaging (BMA) is a method that combines multiple statistical models to make predictions or estimate relationships. Instead of choosing just one best model, it considers all plausible models and weighs each by how well it explains the data, based on their probabilities. This approach accounts for model uncertainty, leading to more robust and reliable results. Think of it like consulting multiple experts and giving more importance to those with better track records, rather than relying on a single opinion. BMA provides a balanced way to incorporate different models and reduce potential biases from choosing just one.