Comments (4)
kuijken
commented on September 17, 2024
Shahb was just talking about this in the context of his attempts to reproduce the DES posteriors. He found the same, a traditional MCMC sampler gives somewhat different results than MiultiNest. He mentioned that there seems to be a consensus that PolyChord is a superior alternative to MultiNest.
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cheymans
commented on September 17, 2024
A shame that PolyChord is not public - although footnote 3 suggests that it will be soon. Joe has built in the fast-slow parameter sampling in CosmoSIS recently though, so it could be that the methodology already exists.
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hendrik1008
commented on September 17, 2024
In KV450 we saw something that might be related. When fitting 5 independent sigma_m and delta_c parameters for the five tomographic bins on top of the 14 parameters that were used in the paper the chains didn't converge. My feeling is that "high-dimensional" is somewhere around 20 dimensions. But this might depend on priors and the nature of the parameters.
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fkoehlin
commented on September 17, 2024
Hi all,
I just want to note that in the most recent MontePython (v3.1)
<https://github.com/brinckmann/montepython_public> it is already possible
to use PolyChord as a sampler. Hence, I think that the PolyChord package
should be public (but might not have been built into CosmoSIS...).
Cheers,
Fabian
Am Fr., 8. März 2019 um 07:47 Uhr schrieb hendrik1008 <
[email protected]>:
… In KV450 we saw something that might be related. When fitting 5
independent sigma_m and delta_c parameters for the five tomographic bins on
top of the 14 parameters that were used in the paper the chains didn't
converge. My feeling is that "high-dimensional" is somewhere around 20
dimensions. But this might depend on priors and the nature of the
parameters.
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