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arviz.from_numpyro — ArviZ dev documentation

arviz.from_numpyro(posterior=None, *, prior=None, posterior_predictive=None, predictions=None, constant_data=None, predictions_constant_data=None, log_likelihood=None, index_origin=None, coords=None, dims=None, pred_dims=None, extra_event_dims=None, num_chains=1)[source]#

Convert NumPyro data into an InferenceData object.

If no dims are provided, this will infer batch dim names from NumPyro model plates. For event dim names, such as with the ZeroSumNormal, infer={"event_dims":dim_names} can be provided in numpyro.sample, i.e.:

# equivalent to dims entry, {"gamma": ["groups"]}
gamma = numpyro.sample(
    "gamma",
    dist.ZeroSumNormal(1, event_shape=(n_groups,)),
    infer={"event_dims":["groups"]}
)

There is also an additional extra_event_dims input to cover any edge cases, for instance deterministic sites with event dims (which dont have an infer argument to provide metadata).

For a usage example read the Creating InferenceData section on from_numpyro

Parameters:

posteriornumpyro.mcmc.MCMC

Fitted MCMC object from NumPyro

prior: dict

Prior samples from a NumPyro model

posterior_predictivedict

Posterior predictive samples for the posterior

predictions: dict

Out of sample predictions

constant_data: dict

Dictionary containing constant data variables mapped to their values.

predictions_constant_data: dict

Constant data used for out-of-sample predictions.

index_originint, optional

coordsdict[str] -> list[str]

Map of dimensions to coordinates

dimsdict[str] -> list[str]

Map variable names to their coordinates. Will be inferred if they are not provided.

pred_dims: dict

Dims for predictions data. Map variable names to their coordinates. Default behavior is to infer dims if this is not provided

num_chains: int

Number of chains used for sampling. Ignored if posterior is present.

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