pymc.compute_log_prior#

pymc.compute_log_prior(idata, var_names=None, extend_inferencedata=True, model=None, sample_dims=('chain', 'draw'), progressbar=True, compile_kwargs=None)[source]#

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
compile_kwargsdict[str, Any] | None

Extra compilation arguments to supply to compute_log_density()

Returns:
idataInferenceData

InferenceData with log_prior group