class_docstr: Class to calculate probability (pmf) values specific to Bayesian Models
docstr: Compute the total log probability density under the model.
  
  Parameters
  ----------
  data: pandas dataframe OR array_like, shape (n_samples, n_features)
      List of n_features-dimensional data points.  Each row
      corresponds to a single data point.
  
  ordering: list
      ordering of columns in data, used by the Bayesian model.
      default is topological ordering used by model.
  
  Returns
  -------
  Log-likelihood of data: float
      This is normalized to be a probability density, so the value
      will be low for high-dimensional data.
code: def score(self, data, ordering=None):
      """
      Compute the total log probability density under the model.
  
      Parameters
      ----------
      data: pandas dataframe OR array_like, shape (n_samples, n_features)
          List of n_features-dimensional data points.  Each row
          corresponds to a single data point.
  
      ordering: list
          ordering of columns in data, used by the Bayesian model.
          default is topological ordering used by model.
  
      Returns
      -------
      Log-likelihood of data: float
          This is normalized to be a probability density, so the value
          will be low for high-dimensional data.
      """
      return np.sum(self.log_probability(data, ordering))
  
file_path: pgmpy/pgmpy/metrics/bn_inference.py
imports: import numpy
  import pandas
  from pgmpy.sampling import BayesianModelInference
label_desc: Use image node_id 4 for calling the BayesianModelProbability obj's underlying member method code with example usage: obj.score(data, ordering) and returns: np