openghg_inversions.rhime.sampling

RHIME sampling helpers.

class openghg_inversions.rhime.sampling.RhimeSampler(*, draws: int = 1000, burn: int = 0, tune: int = 1000, chains: int = 4, nuts_sampler: Literal['pymc', 'nutpie', 'numpyro', 'blackjax'] | str = 'pymc', progressbar: bool = False, sample_kwargs: dict[str, Any] | None = None, sample_prior_predictive: bool | int = True, sample_posterior_predictive: bool | Sequence[str] = ('y',), posterior_predictive_kwargs: dict[str, Any] | None = None)

Bases: object

PyMC sampler configuration and execution for RHIME models.

Parameters:

• draws – Number of post-tuning draws requested from PyMC.

• burn – Number of draws to discard from each chain after sampling.

• tune – Number of PyMC tuning draws.

• chains – Number of MCMC chains.

• nuts_sampler – PyMC NUTS backend name.

• progressbar – Whether PyMC progress output should be shown.

• sample_kwargs – Extra keyword arguments forwarded to pm.sample.

• sample_prior_predictive – Whether to append prior predictive draws.

• sample_posterior_predictive – Whether to append posterior predictive draws, or variable names to sample.

• posterior_predictive_kwargs – Extra keyword arguments forwarded to pm.sample_posterior_predictive.

burn: int

chains: int

draws: int

nuts_sampler: Literal['pymc', 'nutpie', 'numpyro', 'blackjax']

posterior_predictive_kwargs: dict[str, Any] | None

progressbar: bool

sample(model: Model) → InferenceData

Sample a built RHIME model and append requested predictive groups.

sample_kwargs: dict[str, Any] | None

sample_posterior_predictive: bool | tuple[str, ...]

sample_prior_predictive: bool | int

tune: int