https://github.com/bilby-dev/nessai-bilby
Interface and plugin for using nessai in bilby
https://github.com/bilby-dev/nessai-bilby
bayesian-inference nested-sampling
Last synced: 4 months ago
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Interface and plugin for using nessai in bilby
- Host: GitHub
- URL: https://github.com/bilby-dev/nessai-bilby
- Owner: bilby-dev
- License: mit
- Created: 2023-11-06T13:57:17.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-03T19:00:17.000Z (7 months ago)
- Last Synced: 2025-07-01T16:51:17.479Z (6 months ago)
- Topics: bayesian-inference, nested-sampling
- Language: Python
- Homepage:
- Size: 33.2 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# nessai-bilby
Interface and plugin for using `nessai` in `bilby`.
This plugin provides two samplers that can be used in `bilby`:
- `nessai`: the standard nested sampler from `nessai`
- `inessai`: the importance nested sampler from nessai
It also provides a means to use `bilby` likelihoods and priors directly in
`nessai`, see [using bilby likelihoods in nessai](#using-bilby-likelihoods-in-nessai)
## Installation
The package can be installed using pip
```
pip install nessai-bilby
```
or conda
```
conda install conda-forge::nessai-bilby
```
However, we recommend following installing PyTorch manually to ensure the
correct device support.
**Note:** this plugin requires "bilby>=2.3.0".
## Usage
### In bilby
One `nessai-bilby` is installed, both samplers can be used directly in `bilby`
via the `run_sampler` function. See the bilby documentation for more details
on how to run different samplers.
### Using bilby likelihoods in nessai
`nessai-bilby` also provides two model classes that allow bilby likelihood and
priors to be used directly with nessai:
- `nessai_bilby.model.BilbyModel`:
- `nessai_bilby.model.BilbyModelLikelihoodConstraint`:
Either model can be used by creating an instance of the model and running `nessai` as usual:
```python
from nessai.flowsampler import FlowSampler
from nessai_bilby.model import BilbyModel
likelihood = ... # bilby likelihood object
priors = ... # bilby PriorDict
model = BilbyModel(
priors=priors,
likelihood=likelihood,
use_ratio=True # Whether to use the log-likelihood ratio
)
fs = FlowSampler(
model,
...,
)
fs.run()
```
## Citing
If you use `nessai-bilby`, please cite the `nessai` and `bilby` code bases and the corresponding papers.