MCMCProcess

class dynode.infer.inference.MCMCProcess(*, numpyro_model: ~typing.Callable[[~dynode.config.simulation_config.SimulationConfig, ~typing.Tuple[~jax.Array, ...] | ~jax.Array | None], ~diffrax._solution.Solution], inference_prngkey: ~jax.Array = Array([      0, 8675314], dtype=uint32), num_samples: ~typing.Annotated[int, ~annotated_types.Gt(gt=0)], num_warmup: ~typing.Annotated[int, ~annotated_types.Gt(gt=0)], num_chains: ~typing.Annotated[int, ~annotated_types.Gt(gt=0)], nuts_max_tree_depth: ~typing.Annotated[int, ~annotated_types.Gt(gt=0)], nuts_init_strategy: ~typing.Callable = <function init_to_median>, mcmc_kwargs: dict = <factory>, nuts_kwargs: dict = <factory>, progress_bar: bool = True)

Bases: InferenceProcess

Inference process for fitting a numpyro_model to data using MCMC.

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

get_samples([group_by_chain, ...])	Get the posterior samples from the inference process.
infer(**kwargs)	Fit the numpyro_model to data using MCMC.
model_post_init(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
to_arviz()	Return the results of a fit as an arviz InferenceData object.

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
num_samples
num_warmup
num_chains
nuts_max_tree_depth
nuts_init_strategy
mcmc_kwargs
nuts_kwargs
progress_bar
numpyro_model
inference_prngkey

num_samples: Annotated[int, Gt(gt=0)]

num_warmup: Annotated[int, Gt(gt=0)]

num_chains: Annotated[int, Gt(gt=0)]

nuts_max_tree_depth: Annotated[int, Gt(gt=0)]

nuts_init_strategy: Callable

mcmc_kwargs: dict

nuts_kwargs: dict

progress_bar: bool

infer(**kwargs) → MCMC

Fit the numpyro_model to data using MCMC.

Additional keyword arguments are passed to the numpyro_model.

Returns

MCMC

The MCMC object used for inference.

get_samples(group_by_chain=False, exclude_deterministic=True) → dict[str, Array]

Get the posterior samples from the inference process.

Parameters

group_by_chainbool

whether or not to group posterior samples by chain or not. Adds a leading dimension to return dict’s values if True.

exclude_deterministicbool

whether or not to exclude parameters generated from numpyro.deterministic as keys in the returned dictionary, by default True.

Returns

dict[str, Array] A dictionary of posterior samples, where keys are parameter sites and values are the corresponding samples, arranged with shape (num_chains * num_samples,) if group_by_chain=False, otherwise arranged by (num_chains, num_samples).

to_arviz() → InferenceData

Return the results of a fit as an arviz InferenceData object.

Returns

arviz.InferenceData

arviz InferenceData object containing both priors and posterior_predictive.

Raises

AssertionError

if fitting has not yet been run via infer()

_abc_impl = <_abc._abc_data object>

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.