def to_markov_model(self):
        """
        Converts the factor graph into markov model.

        A markov model contains nodes as random variables and edge between
        two nodes imply interaction between them.

        Examples
        --------
        >>> from pgmpy.models import FactorGraph
        >>> from pgmpy.factors.discrete import DiscreteFactor
        >>> G = FactorGraph()
        >>> G.add_nodes_from(['a', 'b', 'c'])
        >>> phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
        >>> phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
        >>> G.add_factors(phi1, phi2)
        >>> G.add_nodes_from([phi1, phi2])
        >>> G.add_edges_from([('a', phi1), ('b', phi1),
        ...                   ('b', phi2), ('c', phi2)])
        >>> mm = G.to_markov_model()
        """
        mm = MarkovModel()

        variable_nodes = self.get_variable_nodes()

        if len(set(self.nodes()) - set(variable_nodes)) != len(self.factors):
            raise ValueError('Factors not associated with all the factor nodes.')

        mm.add_nodes_from(variable_nodes)
        for factor in self.factors:
            scope = factor.scope()
            mm.add_edges_from(itertools.combinations(scope, 2))
            mm.add_factors(factor)

        return mm