class_docstr: Scale prior distributions parameters.
class_docstr_tok: ["Scale","prior","distributions","parameters","."]
code: def get_intercept_stats(self):
      mu = self.response_mean
      sigma = self.STD * self.response_std
  
      # Only adjust mu and sigma if there is at least one Normal prior for a common term.
      if self.priors:
          sigmas = np.hstack(
              [prior["sigma"] for prior in self.priors.values()]
          )
          x_mean = np.hstack(
              [
                  self.response_component.terms[term].data.mean(axis=0)
                  for term in self.priors
              ]
          )
          sigma = (sigma**2 + np.dot(sigmas**2, x_mean**2)) ** 0.5
  
      return mu, 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 2 for calling the PriorScaler obj's underlying member method code with example usage: obj.get_intercept_stats() and returns: mu,  sigma