Method to estimate the CPD for a given variable.

        Parameters
        ----------
        node: int, string (any hashable python object)
            The name of the variable for which the CPD is to be estimated.

        prior_type: 'dirichlet', 'BDeu', 'K2',
            string indicting which type of prior to use for the model parameters.
            - If 'prior_type' is 'dirichlet', the following must be provided:
                'pseudo_counts' = dirichlet hyperparameters; 2-D array of shape
                 (node_card, product of parents_card) with a "virtual" count for 
                 each variable state in the CPD.
                 The virtual counts are added to the actual state counts found in the data.
                 (if a list is provided, a lexicographic ordering of states is assumed)
            - If 'prior_type' is 'BDeu', then an 'equivalent_sample_size'
                must be specified instead of 'pseudo_counts'. This is equivalent to
                'prior_type=dirichlet' and using uniform 'pseudo_counts' of
                `equivalent_sample_size/(node_cardinality*np.prod(parents_cardinalities))`.
            - A prior_type of 'K2' is a shorthand for 'dirichlet' + setting every pseudo_count to 1,
                regardless of the cardinality of the variable.

        Returns
        -------
        CPD: TabularCPD

        Examples
        --------
        >>> import pandas as pd
        >>> from pgmpy.models import BayesianModel
        >>> from pgmpy.estimators import BayesianEstimator
        >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]})
        >>> model = BayesianModel([('A', 'C'), ('B', 'C')])
        >>> estimator = BayesianEstimator(model, data)
        >>> cpd_C = estimator.estimate_cpd('C', prior_type="dirichlet", pseudo_counts=[1, 2])
        >>> print(cpd_C)
        ╒══════╤══════╤══════╤══════╤════════════════════╕
        │ A    │ A(0) │ A(0) │ A(1) │ A(1)               │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ B    │ B(0) │ B(1) │ B(0) │ B(1)               │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ C(0) │ 0.25 │ 0.25 │ 0.5  │ 0.3333333333333333 │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ C(1) │ 0.75 │ 0.75 │ 0.5  │ 0.6666666666666666 │
        ╘══════╧══════╧══════╧══════╧════════════════════╛

def estimate_cpd(self, node, prior_type='BDeu', pseudo_counts=[], equivalent_sample_size=5):
        """
        Method to estimate the CPD for a given variable.

        Parameters
        ----------
        node: int, string (any hashable python object)
            The name of the variable for which the CPD is to be estimated.

        prior_type: 'dirichlet', 'BDeu', 'K2',
            string indicting which type of prior to use for the model parameters.
            - If 'prior_type' is 'dirichlet', the following must be provided:
                'pseudo_counts' = dirichlet hyperparameters; 2-D array of shape
                 (node_card, product of parents_card) with a "virtual" count for 
                 each variable state in the CPD.
                 The virtual counts are added to the actual state counts found in the data.
                 (if a list is provided, a lexicographic ordering of states is assumed)
            - If 'prior_type' is 'BDeu', then an 'equivalent_sample_size'
                must be specified instead of 'pseudo_counts'. This is equivalent to
                'prior_type=dirichlet' and using uniform 'pseudo_counts' of
                `equivalent_sample_size/(node_cardinality*np.prod(parents_cardinalities))`.
            - A prior_type of 'K2' is a shorthand for 'dirichlet' + setting every pseudo_count to 1,
                regardless of the cardinality of the variable.

        Returns
        -------
        CPD: TabularCPD

        Examples
        --------
        >>> import pandas as pd
        >>> from pgmpy.models import BayesianModel
        >>> from pgmpy.estimators import BayesianEstimator
        >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]})
        >>> model = BayesianModel([('A', 'C'), ('B', 'C')])
        >>> estimator = BayesianEstimator(model, data)
        >>> cpd_C = estimator.estimate_cpd('C', prior_type="dirichlet", pseudo_counts=[1, 2])
        >>> print(cpd_C)
        ╒══════╤══════╤══════╤══════╤════════════════════╕
        │ A    │ A(0) │ A(0) │ A(1) │ A(1)               │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ B    │ B(0) │ B(1) │ B(0) │ B(1)               │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ C(0) │ 0.25 │ 0.25 │ 0.5  │ 0.3333333333333333 │
        ├──────┼──────┼──────┼──────┼────────────────────┤
        │ C(1) │ 0.75 │ 0.75 │ 0.5  │ 0.6666666666666666 │
        ╘══════╧══════╧══════╧══════╧════════════════════╛
        """

        node_cardinality = len(self.state_names[node])
        parents = sorted(self.model.get_parents(node))
        parents_cardinalities = [len(self.state_names[parent]) for parent in parents]
        cpd_shape = (node_cardinality, np.prod(parents_cardinalities, dtype=int))

        if prior_type == 'K2':
            pseudo_counts = np.ones(cpd_shape, dtype=int)
        elif prior_type == 'BDeu':
            alpha = float(equivalent_sample_size) / (node_cardinality * np.prod(parents_cardinalities))
            pseudo_counts = np.ones(cpd_shape, dtype=float) * alpha
        elif prior_type == 'dirichlet':
            pseudo_counts = np.array(pseudo_counts)
            if pseudo_counts.shape != cpd_shape:
                raise ValueError("The shape of pseudo_counts must be: {shape}".format(
                   shape=str(cpd_shape)))
        else:
            raise ValueError("'prior_type' not specified")

        state_counts = self.state_counts(node)
        bayesian_counts = state_counts + pseudo_counts

        cpd = TabularCPD(node, node_cardinality, np.array(bayesian_counts),
                         evidence=parents,
                         evidence_card=parents_cardinalities,
                         state_names=self.state_names)
        cpd.normalize()
        return cpd