Implementation of nessai: nested sampling with artificial intelligence in PyTorch.
nessai-torch can be install using pip:
pip install nessai-torch
We recommend installing PyTorch first to ensure the version is compatible with your system.
nessai-torch has a different API to nessai, the user must define a
log-likelihood function and a prior-transform function instead of a model
object. It also has a reduced feature set compared to standard nessai.
The basic usage is shown below, for a more complete example, see the
examples directory.
from nessai_torch.sampler import Sampler
# Define the log-likelihood and prior transform
...
sampler = Sampler(
log_likelihood=log_likelihood_fn,
prior_transform=prior_transform_fn,
dims=dims, # Number of dimensions
)
# Run the sampler
sampler.run()Note that both the log_likelihood and prior_transform must be vectorized.
If you use nessai-torch in your work please cite the DOI and the relevant papers:
@article{Williams_2021,
doi = {10.1103/physrevd.103.103006},
url = {https://doi.org/10.1103%2Fphysrevd.103.103006},
year = 2021,
month = {may},
publisher = {American Physical Society ({APS})},
volume = {103},
number = {10},
author = {Michael J. Williams and John Veitch and Chris Messenger},
title = {Nested sampling with normalizing flows for gravitational-wave inference},
journal = {Physical Review D}
}
@article{Williams_2023,
doi = {10.1088/2632-2153/acd5aa},
url = {https://doi.org/10.1088%2F2632-2153%2Facd5aa},
year = 2023,
month = {jul},
publisher = {{IOP} Publishing},
volume = {4},
number = {3},
pages = {035011},
author = {Michael J. Williams and John Veitch and Chris Messenger},
title = {Importance nested sampling with normalising flows},
journal = {Machine Learning: Science and Technology}
}