Open problems for Bayesians

So, catching my breath a bit this week and have had some time for a little background reading. In particular, I enjoyed this column on open problems in the Bayesian space. I particularly was intrigued by the quote:

Several respondents asked for a more thorough integration of computational science and statistical science, noting that the set of inferences that one can reach in any given situation are jointly a function of the model, the prior, the data and the computational resources, and wishing for more explicit management of the tradeoffs among these quantities.

While I've never framed it this way myself, this is a pretty good summation of where my head is going into my research. There are questions that we want to ask, data we can use to answer, and biases that come from our assumptions. But, nobody seems to concern themselves with the skewness that come from the actual mechanics of deriving the answer. Most people just assume that the effect is negligible (or simply don't acknowledge it at all). However, as the provider of such systems, I can assure you that the effect is material.

In particular, if I can't build you a system that gets you a good answer fast enough, you will use a bad answer. That's my goal in a nutshell: good (not perfect) answers given in minimal time.