Query method for Dynamic Bayesian Network using Interface Algorithm.

        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

        Examples:
        --------
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> from pgmpy.models import DynamicBayesianNetwork as DBN
        >>> from pgmpy.inference import DBNInference
        >>> dbnet = DBN()
        >>> dbnet.add_edges_from([(('Z', 0), ('X', 0)), (('X', 0), ('Y', 0)),
        ...                       (('Z', 0), ('Z', 1))])
        >>> z_start_cpd = TabularCPD(('Z', 0), 2, [[0.5, 0.5]])
        >>> x_i_cpd = TabularCPD(('X', 0), 2, [[0.6, 0.9],
        ...                                    [0.4, 0.1]],
        ...                      evidence=[('Z', 0)],
        ...                      evidence_card=[2])
        >>> y_i_cpd = TabularCPD(('Y', 0), 2, [[0.2, 0.3],
        ...                                    [0.8, 0.7]],
        ...                      evidence=[('X', 0)],
        ...                      evidence_card=[2])
        >>> z_trans_cpd = TabularCPD(('Z', 1), 2, [[0.4, 0.7],
        ...                                        [0.6, 0.3]],
        ...                      evidence=[('Z', 0)],
        ...                      evidence_card=[2])
        >>> dbnet.add_cpds(z_start_cpd, z_trans_cpd, x_i_cpd, y_i_cpd)
        >>> dbnet.initialize_initial_state()
        >>> dbn_inf = DBNInference(dbnet)
        >>> dbn_inf.query([('X', 0)], {('Y', 0):0, ('Y', 1):1, ('Y', 2):1})[('X', 0)].values
        array([ 0.66594382,  0.33405618])

def query(self, variables, evidence=None, args='exact'):
        """
        Query method for Dynamic Bayesian Network using Interface Algorithm.

        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

        Examples:
        --------
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> from pgmpy.models import DynamicBayesianNetwork as DBN
        >>> from pgmpy.inference import DBNInference
        >>> dbnet = DBN()
        >>> dbnet.add_edges_from([(('Z', 0), ('X', 0)), (('X', 0), ('Y', 0)),
        ...                       (('Z', 0), ('Z', 1))])
        >>> z_start_cpd = TabularCPD(('Z', 0), 2, [[0.5, 0.5]])
        >>> x_i_cpd = TabularCPD(('X', 0), 2, [[0.6, 0.9],
        ...                                    [0.4, 0.1]],
        ...                      evidence=[('Z', 0)],
        ...                      evidence_card=[2])
        >>> y_i_cpd = TabularCPD(('Y', 0), 2, [[0.2, 0.3],
        ...                                    [0.8, 0.7]],
        ...                      evidence=[('X', 0)],
        ...                      evidence_card=[2])
        >>> z_trans_cpd = TabularCPD(('Z', 1), 2, [[0.4, 0.7],
        ...                                        [0.6, 0.3]],
        ...                      evidence=[('Z', 0)],
        ...                      evidence_card=[2])
        >>> dbnet.add_cpds(z_start_cpd, z_trans_cpd, x_i_cpd, y_i_cpd)
        >>> dbnet.initialize_initial_state()
        >>> dbn_inf = DBNInference(dbnet)
        >>> dbn_inf.query([('X', 0)], {('Y', 0):0, ('Y', 1):1, ('Y', 2):1})[('X', 0)].values
        array([ 0.66594382,  0.33405618])
        """
        if args == 'exact':
            return self.backward_inference(variables, evidence)