Comments (3)
I'll investigate.
from uncertaintyquantification.jl.
Turns out the issue here is using a sample size where N*target
leads to less samples in the levels >=2
. For example using 205 and 0.1 leads to 189 samples in the subsets because of the way the number of chains and samples per chain are calculated
number_of_seeds = Int64(max(1, ceil(sim.n * sim.target)))
samples_per_seed = Int64(floor(sim.n / number_of_seeds))
here 21 seeds and 9 chains = 189 != 205.
However the sample size passed to the cov estimation is always n
.
This is easily fixed by computing n
for the estimation from the available samples.
from uncertaintyquantification.jl.
Next time please just paste the code here instead of uploading a zip file.
from uncertaintyquantification.jl.
Related Issues (20)
- ExternalModel - Absolute Path is required
- Randomized quasi Monte Carlo HOT 1
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