class_docstr: Abstract base class for structure scoring classes in pgmpy. Use any of the derived classes
  K2Score, BDeuScore, BicScore or AICScore. Scoring classes are
  used to measure how well a model is able to describe the given data set.
  
  Parameters
  ----------
  data: pandas DataFrame object
      dataframe object where each column represents one variable.
      (If some values in the data are missing the data cells should be set to `numpy.NaN`.
      Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
  
  state_names: dict (optional)
      A dict indicating, for each variable, the discrete set of states (or values)
      that the variable can take. If unspecified, the observed values in the data set
      are taken to be the only possible states.
  
  Reference
  ---------
  Koller & Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009
  Section 18.3
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docstr: A (log) prior distribution over models. Currently unused (= uniform).
docstr_tok: ["A","(","log",")","prior","distribution","over","models",".","Currently","unused","(","=","uniform",")","."]
code: def structure_prior(self, model):
      """A (log) prior distribution over models. Currently unused (= uniform)."""
      return 0
  
imports: from math import lgamma, log
  import numpy
  from scipy.special import gammaln
  from pgmpy.estimators import BaseEstimator
label_desc: Use image node_id 3 for calling the StructureScore obj's underlying member method code with example usage: obj.structure_prior(model) and returns: int