jesterTOV.inference.samplers.blackjax.smc.nuts.BlackJAXSMCNUTSSampler

class BlackJAXSMCNUTSSampler(

likelihood,

prior,

sample_transforms,

likelihood_transforms,

config,

seed=0,

)

Bases: BlackjaxSMCSampler

SMC with NUTS kernel and Hessian-based mass matrix adaptation.

WARNING: This sampler is EXPERIMENTAL. Use with caution and validate results carefully. The NUTS kernel with Hessian adaptation has not been thoroughly tested.

Parameters:

• likelihood (LikelihoodBase) – Likelihood object

• prior (Prior) – Prior object

• sample_transforms (list[BijectiveTransform]) – Sample transforms (typically empty for SMC)

• likelihood_transforms (list[NtoMTransform]) – Likelihood transforms

• config (SMCNUTSSamplerConfig) – NUTS SMC configuration

• seed (int, optional) – Random seed (default: 0)

__init__(

likelihood,

prior,

sample_transforms,

likelihood_transforms,

config,

seed=0,

)

Initialize with EXPERIMENTAL warning.

Methods

__init__(likelihood, prior, ...[, seed])	Initialize with EXPERIMENTAL warning.
add_name(x)	Turn an array into a dictionary.
get_log_prob()	Get log posterior probabilities from SMC.
get_n_samples()	Get number of particles from SMC.
get_sampler_output()	Get standardized sampler output.
get_samples()	Return final particle positions.
plot_diagnostics([outdir, filename])	Generate diagnostic plots for SMC sampling run.
posterior(params, data)	Evaluate posterior log probability from flat array.
posterior_from_dict(named_params, data)	Evaluate posterior log probability from parameter dict.
print_summary([transform])	Print summary of sampling run.
sample(key)	Run SMC until λ = 1 (posterior).

Attributes

config
final_state
metadata
likelihood
prior
sample_transforms
likelihood_transforms
parameter_names
sampler