arviz.from_pyjags#
- arviz.from_pyjags(posterior=None, prior=None, log_likelihood=None, coords=None, dims=None, save_warmup=None, warmup_iterations=0)[source]#
Convert PyJAGS posterior samples to an ArviZ inference data object.
Takes a python dictionary of samples that has been generated by the sample method of a model instance and returns an Arviz inference data object. For a usage example read the Creating InferenceData section on from_pyjags
- Parameters:
- posterior: dict of {strarray_like}, optional
a dictionary mapping variable names to NumPy arrays containing posterior samples with shape (parameter_dimension, chain_length, number_of_chains)
- prior: dict of {strarray_like}, optional
a dictionary mapping variable names to NumPy arrays containing prior samples with shape (parameter_dimension, chain_length, number_of_chains)
- log_likelihood: dict of {str: str}, list of str or str, optional
Pointwise log_likelihood for the data. log_likelihood is extracted from the posterior. It is recommended to use this argument as a dictionary whose keys are observed variable names and its values are the variables storing log likelihood arrays in the JAGS code. In other cases, a dictionary with keys equal to its values is used.
- coords: dict[str, iterable]
A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values.
- dims: dict[str, List(str)]
A mapping from variables to a list of coordinate names for the variable.
- save_warmupbool, optional
Save warmup iterations in InferenceData. If not defined, use default defined by the rcParams.
- warmup_iterations: int, optional
Number of warmup iterations
- Returns: