threeML.bayesian.ultranest_sampler module

class threeML.bayesian.ultranest_sampler.UltraNestSampler(likelihood_model=None, data_list=None, **kwargs)

Bases: UnitCubeSampler

sample(quiet=False)

sample using the UltraNest numerical integration method :rtype:

Returns:

setup(min_num_live_points: int = 400, dlogz: float = 0.5, chain_name: str | None = None, resume: str = 'overwrite', wrapped_params=None, stepsampler=None, use_mlfriends: bool = True, **kwargs)

set up the Ultranest sampler. Consult the documentation:

https://johannesbuchner.github.io/UltraNest/ultranest.html?highlight=reactive#ultranest.integrator.ReactiveNestedSampler

param min_num_live_points:

minimum number of live points throughout the run

type min_num_live_points:

int

param dlogz:

Target evidence uncertainty. This is the std between bootstrapped logz integrators.

type dlogz:

float

param chain_name:

where to store output files

type chain_name:

param resume:

(‘resume’, ‘resume-similar’, ‘overwrite’ or ‘subfolder’) –

if ‘overwrite’, overwrite previous data. if ‘subfolder’, create a fresh subdirectory in log_dir. if ‘resume’ or True, continue previous run if available. Only works when dimensionality, transform or likelihood are consistent. if ‘resume-similar’, continue previous run if available. Only works when dimensionality and transform are consistent. If a likelihood difference is detected, the existing likelihoods are updated until the live point order differs. Otherwise, behaves like resume.

type resume:

str

param wrapped_params:

(list of bools) – indicating whether this parameter wraps around (circular parameter).

type wrapped_params:

param stepsampler:

type stepsampler:

param use_mlfriends:

Whether to use MLFriends+ellipsoidal+tellipsoidal region (better for multi-modal problems) or just ellipsoidal sampling (faster for high-dimensional, gaussian-like problems).

type use_mlfriends:

bool

returns: