> We should ask, “what evidence are you providing that your priors are any good?” This is valid. Anyone pursuing a Bayesian approach should be asking themselv

> We should ask, “what evidence are you providing that your priors are any good?” This is valid. Anyone pursuing a Bayesian approach should be asking themselves this question about every prior they use. To fully benefit from a Bayesian framework, one needs to construct models with understandable parameters for which there is some sound theoretical or practical insight that can be embedded with priors and that is not well-represented by the training data. Doing this can help your solution avoid the kind of wildly unpredictable and costly mistakes you might get if you used a completely blackbox approach. For critical applications, this can be highly useful. If you can't come up with priors that are clearly beneficial, then you are likely better off using a non-Bayesian approach.