bebi103.hmc.parse_prior_predictive_samples

bebi103.hmc.parse_prior_predictive_samples(prior_pred_samples, measured_data)

Given a dictionary of prior predictive samples, convert them to a dictionary that can use used in inferential calibration analysis.

Parameters:

• prior_pred_samples (dict) – Dictionary that has a prior predictive sample. Entries with keys that are not in measured_data are assumed to be variables drawn out of the prior distribution.

• measured_data (dict) – A dictionary where each key is the variable name of measured data. The value of each entry in the measured_data dictionary is the type of the corresponding data, almost always either int or float. Upon generation of a prior predictive data set, a new dictionary is created with keys given by the keys of measured_data and value given by the generated prior predictive data set.

Returns:

output – All variables (that is, those not in measured data) are broken down into individual components (e.g., ‘a[1]’, ‘a[2]’, etc.). Any entry that is in measured_data is given as a scalar or Numpy array.

Return type:

dict