Do we still need to use domain knowledge when doing Bayesian Inference using MCMC? I am learning MCMC for the purpose of doing Bayesian inference. In Andrieu, 2003, it is mentioned that:

... in order to obtain the best results out of this class of algorithms, it is important that we do not treat them as black boxes, but instead try to incorporate as much domain specific knowledge as possible into their design.

The article was written 15 years ago. I am aware that now we have packages like Stan and JAGS (in R) that relieve users from worry about how to set the parameters of various MCMC algorithms to achieve best mixing. 
My question is: Is domain knowledge still relevant when doing Bayesian inference using MCMC? (Edit: Do we still need to use domain knowledge to tweak the various algorithms, like choosing the proposal distribution, or setting the tuning parameters?) 
If your answer is yes, could you give one example of where domain knowledge had helped you achieving better results? The more specific, the better.
 A: MCMC algorithms have improving since Andrieu (2003), and we now have the NUTS sampler in Stan, which is automated so that it can be used in applied problems without the user needing to understand the underlying algorithm.  It is probably true that some difficult cases will require custom algorithms to run efficiently, but there is a trade-off between computational efficiency and human thinking time.  In regard to this issue, there is a great quote by the mathematician and philosopher Alfred Whitehead that is one of my favourite pearls of wisdom:

"It is a profoundly erroneous truism, repeated by all copy-books and by eminent people when they are making speeches, that we should cultivate the habit of thinking what we are doing.  The precise opposite is the case.  Civilisation advances by extending the number of important operations which we can perform without thinking about them.  Operations of thought are like cavalry charges in a battle — they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.  
--- Whitehead (1911) An Introduction to Mathematics, pp. 45-46.

A: I agreed that it is unclear on the focus of the initial inquiry given the usage of the term "mixture". However, I will also point out that some individuals may opt to use Bayesian models with non-informative priors not to incorporate prior information, but to get estimates that may be deemed more interpretable to their general audience. For example, the difference between frequentist versus the Bayesian model output interpretations. This would be an approach not based on prior domain knowledge though uses Bayesian modeling. 
