Parameters
        ----------
        variables: list
            list of variables for which you want to compute the probability

        evidence: dict
            a dict key, value pair as {var: state_of_var_observed}
            None if no evidence

        elimination_order: list
            order of variable eliminations (if nothing is provided) order is
            computed automatically

        joint: boolean (default: True)
            If True, returns a Joint Distribution over `variables`.
            If False, returns a dict of distributions over each of the `variables`.

        Examples
        --------
        >>> from pgmpy.inference import VariableElimination
        >>> from pgmpy.models import BayesianModel
        >>> import numpy as np
        >>> import pandas as pd
        >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
        ...                       columns=['A', 'B', 'C', 'D', 'E'])
        >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
        >>> model.fit(values)
        >>> inference = VariableElimination(model)
        >>> phi_query = inference.query(['A', 'B'])

def query(self, variables, evidence=None, elimination_order=None, joint=True):
        """
        Parameters
        ----------
        variables: list
            list of variables for which you want to compute the probability

        evidence: dict
            a dict key, value pair as {var: state_of_var_observed}
            None if no evidence

        elimination_order: list
            order of variable eliminations (if nothing is provided) order is
            computed automatically

        joint: boolean (default: True)
            If True, returns a Joint Distribution over `variables`.
            If False, returns a dict of distributions over each of the `variables`.

        Examples
        --------
        >>> from pgmpy.inference import VariableElimination
        >>> from pgmpy.models import BayesianModel
        >>> import numpy as np
        >>> import pandas as pd
        >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
        ...                       columns=['A', 'B', 'C', 'D', 'E'])
        >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
        >>> model.fit(values)
        >>> inference = VariableElimination(model)
        >>> phi_query = inference.query(['A', 'B'])
        """
        return self._variable_elimination(variables, 'marginalize',
                                          evidence=evidence, elimination_order=elimination_order,
                                          joint=joint)