Ns aw
BlackJAX nested sampling with bilby/dynesty-style adaptive DE acceptance-walk kernel.
BlackJAXNSAWSampler
¤
Bases: Sampler
BlackJAX nested sampler using the bilby/dynesty-style adaptive DE acceptance-walk kernel.
Samples in the sampling space defined by sample_transforms (typically
the unit cube via BoundToBound transforms). Operates on flat arrays
of shape (n_dims,); the acceptance-walk kernel is pytree-generic and
works identically with flat arrays.
Note
This sampler requires the sampling space to be the unit hypercube
[0, 1]^n_dims. All sample_transforms in Jim must map the
prior support onto [0, 1] per dimension before sampling. A
ValueError is raised at construction if the supplied
log_prior_fn violates this constraint.
Reference: Prathaban, M., Yallup, D., Alvey, J., Yang, M., Templeton, W., Handley, W., "Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns", arXiv:2509.04336 (Sep 2025).
Configure via BlackJAXNSAWConfig.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n_dims
|
int
|
Dimension of the sampling space. |
required |
log_prior_fn
|
Callable
|
Log-prior callable |
required |
log_likelihood_fn
|
Callable
|
Log-likelihood callable |
required |
log_posterior_fn
|
Callable
|
Log-posterior callable |
required |
config
|
Optional[BlackJAXNSAWConfig]
|
Optional |
None
|
periodic
|
Optional[list[int]]
|
Optional list of dimension indices that are periodic in
|
None
|
Methods:
| Name | Description |
|---|---|
get_samples |
Return equally-weighted posterior samples. |
get_samples() -> dict[str, np.ndarray]
¤
Return equally-weighted posterior samples.
Uses NestedSamples.posterior_points to
resample the nested dead-point collection to a set of equal-weight
samples.
Returns:
| Type | Description |
|---|---|
dict[str, ndarray]
|
Dict with keys |
dict[str, ndarray]
|
|