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UltraNest is a very reliable tuning-parameter-free algorithm. I have published some examples where the UltraNest algorithm is unbiased while pymultinest gives a different answer. UltraNest is a pure-python package and very easy to install with pip or conda.
UltraNest also supports resuming from disk and MPI parallelisation, if that is useful to you.
The text was updated successfully, but these errors were encountered:
Hi! I came across your work in the literature and I am quite interested in alternative sampling approaches beyond static Nested Sampling in MultiNest.
I currently wrap MultiNest's C API directly with Cython (see pyx and pxd). This is done principally for performance reasons to avoid Python overhead at various levels. My initial tests using Dynesty showed that this was actually a significant run-time cost, at least for this application of spectral cube model fitting. So at this stage changes to the sampler would be non-trivial to implement, but I would very much like to re-evaluate alternative samplers like UltraNest and Dynesty in the future!
Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ?
The interface should be very similar to pymultinest.
UltraNest is a very reliable tuning-parameter-free algorithm. I have published some examples where the UltraNest algorithm is unbiased while pymultinest gives a different answer. UltraNest is a pure-python package and very easy to install with pip or conda.
UltraNest also supports resuming from disk and MPI parallelisation, if that is useful to you.
The text was updated successfully, but these errors were encountered: