Johannes Buchner
Johannes Buchner
May be useful later with which ultranest version the points file was created, to enable backward compatibility
At the moment the warning that ultranest is progressing slowly is giving a few pointers. However, the frac_remain one is not so useful, if the percentage is still zero. Perhaps...
Dear all, It would be great if you could offer an option to select ultranest as a sampler. UltraNest is a nested sampling package, which provides several algorithms. By default,...
**Describe the bug** I am following https://threeml.readthedocs.io/en/stable/notebooks/grb080916C.html but on the line `fluence_plugin.rebin_on_background(1.0)` I get: ```bash 16:17:31 INFO Auto-determined polynomial order: 0 binned_spectrum_series.py:389 Fitting GBM_NAI_03 background: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:16 62 fluence_plugin.rebin_on_background(1.0)...
Hi @dkogan, Thanks for this very interesting project, I came across it from HN and your talk. 1) I was looking at the syntax for broadcast_define, which is given by...
The about section gives as link: [roban.github.com/CosmoloPy/](http://roban.github.com/CosmoloPy/) The same link is also in the README. The correct link is: https://roban.github.io/CosmoloPy/
A test for biased nested sampling was presented in section 4.5.2 of Buchner (2023, https://arxiv.org/abs/2101.09675). This implements the same idea as https://arxiv.org/abs/2006.03371 except their KS test is sub-optimal because the...
Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ? The interface should be very similar to dynesty and pymultinest. UltraNest is a very reliable tuning-parameter-free algorithm. It can be...
Hi again, I was wondering if you tested this code with CUDA? In the examples it seems GPUs are explicitly disabled via an environment variable. When I remove this restriction,...
Hi Adam, I played with three modifications to the MCMC sampling: 1) Iteratively train the network, sample, train, etc. 2) I store multiple networks during the training process. Then I...