Currently, we input samples from the trained MAF or KDE to calculate the marginal statistics and samples from the original posterior then compare the results to get an error. I think the code needs a bit of a tidy, and it would be good to think about whether there is a better way to get an error on these statistics e.g. some monte carlo error.
I also think that the option to put in prior samples and prior weights to marginal_stats.calculate().statistics() should be scrapped in favour of supplying a prior density estimator so that the user can access all the fun stuff like early stopping and clustering.
Currently, we input samples from the trained MAF or KDE to calculate the marginal statistics and samples from the original posterior then compare the results to get an error. I think the code needs a bit of a tidy, and it would be good to think about whether there is a better way to get an error on these statistics e.g. some monte carlo error.
I also think that the option to put in prior samples and prior weights to
marginal_stats.calculate().statistics()should be scrapped in favour of supplying a prior density estimator so that the user can access all the fun stuff like early stopping and clustering.