The latest advances in Bayesian computing allow users to estimate models that were previously out of reach for most practitioners in industry and academia. Today, we are able to fit models with full Bayesian inference that jointly estimate hundreds of thousands and sometimes millions of parameters. As model complexity grows, we need tools to make sense of these models so we can better understand their strengths and more importantly their weaknesses. By analogy, if we are building a plane, it is our responsibility to test under which conditions it can and cannot fly. We owe this much to our p̶a̶s̶s̶e̶n̶g̶e̶r̶s̶ users.
In this channel, we are focusing on understanding and explaining uncertainty in the broadest sense of the word including interesting model structures, model inferences, predictions, causal inference, decision analysis, and communicating models and uncertainty.