3 ms·
The way I read it, they actually need the randomness introduced from full Monte-Carlo sampling to make sure that they explore the data distribution sufficiently
by fxtentacle 6y ago
The way I read it, they actually need the randomness introduced from full Monte-Carlo sampling to make sure that they explore the data distribution sufficiently far in every direction. So it might be that for this approach, importance sampling would distort the results and re-introduce problems like the chance for divergence that GANs are fighting with.
- alpineidyll3 6y agoThe miracle of importance sampling is that it doesn't formally ever prevent you from visiting the rare regions, it just might take longer. What can occur is that you will get what appears to be a well-converged estimate of an answer but really you weren't fully ergodic. Such is the curse of dimensionality. No free lunch theorem, yadda yadda.