From slide 5 of lecture 2: Explanations with more ways to produce the data are more plausible. Sorry, I disgress, the bias/variance trade off implies that you

From slide 5 of lecture 2: Explanations with more ways to produce the data are more plausible. Sorry, I disgress, the bias/variance trade off implies that you should ponder the complexity of the model to avoid overfitting. I don't know much about Bayesian data analysis, but I think the first idea to be introduced is not the concept of likelihood, Bayes theorem is the foundation and I think it should be introduced before likelihood. On slide 31, it is not clear if you state a causal model before making observations, you should indicate a prior before making the observation so that you can then see how it updates, if done otherwise you are using the observations to elaborate the model and then the analysis is crookely designed. On slide 35, the concept of relative plausibility seems to me a little sloopy to develop some theory. On slide 48: In the context of confidence intervals: "95% is obvious superstition". I think 95% interval is using a standard way to communicate results that gives a useful information and avoid the question of why you computed, for example, the 93.728 confidence interval.