pymc.stats.compute_log_prior

pymc.stats.compute_log_prior(idata, *, var_names=None, extend_inferencedata=True, model=None, sample_dims=('chain', 'draw'), progressbar=True, backend=None, compile_kwargs=None)

Compute elemwise log_prior of model given InferenceData with posterior group.

Parameters:

idataInferenceData

InferenceData with posterior group

var_namessequence of str, optional

List of Observed variable names for which to compute log_prior. Defaults to all all free variables.

extend_inferencedatabool, default True

Whether to extend the original InferenceData or return a new one

modelModel, optional

sample_dimssequence of str, default (“chain”, “draw”)

progressbarbool, default True

backendstr, optional

Which computational backend to use. Recommended to be one of “numba”, “c”, and “jax”.

compile_kwargsdict[str, Any] | None

Extra compilation arguments to supply to compute_log_density(). compile_kwargs["mode"] cannot be combined with backend.

Returns:

idataInferenceData

InferenceData with log_prior group