class_docstr: Base class for parameter estimators in pgmpy.
  
  Parameters
  ----------
  model: pgmpy.models.BayesianNetwork or pgmpy.models.MarkovNetwork or pgmpy.models.NoisyOrModel model
      for which parameter estimation is to be done.
  
  data: pandas DataFrame object
      dataframe object with column names identical to the variable names of the model.
      (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.
docstr: Base class for parameter estimators in pgmpy.
  
  Parameters
  ----------
  model: pgmpy.models.BayesianNetwork or pgmpy.models.MarkovNetwork or pgmpy.models.NoisyOrModel model
      for which parameter estimation is to be done.
  
  data: pandas DataFrame object
      dataframe object with column names identical to the variable names of the model.
      (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.
  
  complete_samples_only: bool (optional, default `True`)
      Specifies how to deal with missing data, if present. If set to `True` all rows
      that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,
      every row where neither the variable nor its parents are `np.NaN` is used.
      This sets the behavior of the `state_count`-method.
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code: def __init__(self, model, data, **kwargs):
      """
      Base class for parameter estimators in pgmpy.
  
      Parameters
      ----------
      model: pgmpy.models.BayesianNetwork or pgmpy.models.MarkovNetwork or pgmpy.models.NoisyOrModel model
          for which parameter estimation is to be done.
  
      data: pandas DataFrame object
          dataframe object with column names identical to the variable names of the model.
          (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.
  
      complete_samples_only: bool (optional, default `True`)
          Specifies how to deal with missing data, if present. If set to `True` all rows
          that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,
          every row where neither the variable nor its parents are `np.NaN` is used.
          This sets the behavior of the `state_count`-method.
      """
      self.model = model
  
      super(ParameterEstimator, self).__init__(data, **kwargs)
  
file_path: pgmpy/pgmpy/estimators/base.py
imports: from collections import OrderedDict
  import pandas
label_desc: Use image node_id 1 to create a new ParameterEstimator object from inherited base classes: BaseEstimator with example: obj = ParameterEstimator(model, data)