Model Comparison

Numerous research questions in basic science are concerned with comparing multiple scientific theories to understand which of them is more likely to be true, or at least closer to the truth. To compare these theories, scientists translate them into statistical models and then investigate how well the models’ predictions match the gathered real-world data.

Even if the goal is purely predictive, model comparison is very important for predictive model selection or averaging. In our lab, we are exploring Bayesian model comparison approaches from both theory-driven and predictive perspectives and even seek to find ways to combine both perspectives.