Mcmc is what takes the time more tham the size of the model.
Definitely large models seem more likely to me in bayesian because it is so much neater to build them.
I notice that you are very fervent in your bayesianism, but please correct me if i read that wrongly. I was once at a conference in which a similar debate was ongoing at the lectern. Dr cox was main guest of the conf and when he was asked where he stood he said, and i misquote probably awfully from poor memory, that it was a bit silly arguing about it because you just used whichever was appropriate to the task at hand. I thought that was pretty cool.
I like Bayesianism because I think it's mathematically and philosophically cleaner. Then again, I think MCMC seems to show that it isn't so computationally clean. In realtime systems I am more than happy to use Frequentist models for their speed.
I suppose believe in a world where Bayesian methods are the primary didactic statistics useful in sciences and communication and Frequentist methods are used when closed form estimators are necessary and coherent with Bayesian estimates.
Or maybe an even better world where we have closed form, useful estimates from both camps.
Just think quantum computing. You could write down everything you know and every piece of data you have, eliciting by hand your utility and priors, and then press go on the mcmc. Awesome.
Definitely large models seem more likely to me in bayesian because it is so much neater to build them.
I notice that you are very fervent in your bayesianism, but please correct me if i read that wrongly. I was once at a conference in which a similar debate was ongoing at the lectern. Dr cox was main guest of the conf and when he was asked where he stood he said, and i misquote probably awfully from poor memory, that it was a bit silly arguing about it because you just used whichever was appropriate to the task at hand. I thought that was pretty cool.