I took your advice and found this
http://www.physics.ox.ac.uk/phystat05/p ... stat05.pdf
Initial results are promising .. 10 hours for 10,000 samples.
Do you have an opinion yourself? Enlighten us. What's new, apart from the cute name?
I bet you won't give a technical answer.
Since you both seem to know virtually nothing about this stuff:
In Bayesian inference you can estimate not only the parameters P of your hypothesised model M, but also perform the model comparison (selection). The full Beyes' rule us p(P | data, M) = p(data | P, M) * p(P | M) / p(data | M), where p(data | M) is called "model evidence". Yo usolve the inverse problem p(M_i | data) = p(data | M_i) / \sum_j p(data | M_j) to find the best model, namely the one with the highest p(M_i | data) (which translates to the highest evidence given data).
Bayesian *neural* networks implement the above procedure in addition to the standard parameter fitting.