arviz.from_numpyro#

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.