maestro,

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.

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