Comments (1)
Ok, I understand what is going on. The UltraNest sampler also relies on numpy's global random state, which means it is drawing the exact same "random" numbers after each step... thus it gets stuck. MultiNest does not suffer because the sampler is not python based: it takes the seed in the initial setup and works with it.
I will correct the logic to avoid this behaviour making the pipeline to generate the ensemble seeds in the beginning (as in the fixed case).
from imagine.
Related Issues (20)
- examples directory needs checking HOT 2
- "Function" Fields HOT 1
- Old imagine repository HOT 8
- Pipeline saving, and resuming after interruption HOT 3
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from imagine.