class_docstr: Scale prior distributions parameters.
class_docstr_tok: ["Scale","prior","distributions","parameters","."]
code: def scale_common(self, term):
      if term.prior.name != "Normal":
          return
  
      # It can be greater than 1 for categorical variables
      if term.data.ndim == 1:
          mu = 0
          sigma = self.get_slope_sigma(term.data)
      else:
          mu = np.zeros(term.data.shape[1])
          sigma = np.zeros(term.data.shape[1])
          # Iterate over columns in the data
          for i, value in enumerate(term.data.T):
              sigma[i] = self.get_slope_sigma(value)
  
      # Save and set prior
      self.priors.update({term.name: {"mu": mu, "sigma": sigma}})
      term.prior.update(mu=mu, sigma=sigma)
  
file_path: bambi/bambi/priors/scaler.py
imports: import numpy
  import pymc
  from bambi.families.univariate import Cumulative, Gaussian, StoppingRatio, StudentT, VonMises
  from bambi.model_components import ConstantComponent
  from bambi.priors.prior import Prior
label_desc: Use image node_id 6 for calling the PriorScaler obj's underlying member method code with example usage: obj.scale_common(term) without return types