What these have in common is that you need to identify the most promising set of candidates to follow up with a limited set of resources. So think top 10 rather than a discretized yes/no.
Thanks for the suggestion! I’ll discuss this possibility with the customer.
Just be careful – you’ll need about 10,000 effective samples to pin down the 1% and 99% quantiles well enough, not just 10,000 samples.
A very good point, Mike! I guess I’m fine so far with the current result: the number of effective samples was 2000 out of 2000 draws for the effect of interest. However, the number of effective samples was pretty low (250-400) for an effect I’m not interested: Does this indicate anything inappropriate for the model or parameterization overall?