bayesian line-fitting

In my writing on fitting a straight line to data (!), I confronted today the issue that Bayesian analyses return distributions not answers and the investigator is forced to decide how to present those results. I am a strong supporter of returning a sampling from the posterior distribution, but that is expensive when your model has thousands of parameters! The standard practice is to return the maximum a posteriori values, which is the quasi-Bayesian answer to maximum likelihood. Not sure how I feel about that; I think it may only make sense at high signal-to-noise, which, as I have commented before, only holds when it doesn't matter what you do.