Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.

        Returns
        -------
        parameters: list
            List of TabularCPDs, one for each variable of the model

        Examples
        --------
        >>> import numpy as np
        >>> import pandas as pd
        >>> from pgmpy.models import BayesianModel
        >>> from pgmpy.estimators import MaximumLikelihoodEstimator
        >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 4)),
        ...                       columns=['A', 'B', 'C', 'D'])
        >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D'))
        >>> estimator = MaximumLikelihoodEstimator(model, values)
        >>> estimator.get_parameters()
        [<TabularCPD representing P(C:2) at 0x7f7b534251d0>,
        <TabularCPD representing P(B:2 | C:2, A:2) at 0x7f7b4dfd4da0>,
        <TabularCPD representing P(A:2) at 0x7f7b4dfd4fd0>,
        <TabularCPD representing P(D:2 | C:2) at 0x7f7b4df822b0>]

def get_parameters(self):
        """
        Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.

        Returns
        -------
        parameters: list
            List of TabularCPDs, one for each variable of the model

        Examples
        --------
        >>> import numpy as np
        >>> import pandas as pd
        >>> from pgmpy.models import BayesianModel
        >>> from pgmpy.estimators import MaximumLikelihoodEstimator
        >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 4)),
        ...                       columns=['A', 'B', 'C', 'D'])
        >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D'))
        >>> estimator = MaximumLikelihoodEstimator(model, values)
        >>> estimator.get_parameters()
        [<TabularCPD representing P(C:2) at 0x7f7b534251d0>,
        <TabularCPD representing P(B:2 | C:2, A:2) at 0x7f7b4dfd4da0>,
        <TabularCPD representing P(A:2) at 0x7f7b4dfd4fd0>,
        <TabularCPD representing P(D:2 | C:2) at 0x7f7b4df822b0>]
        """
        parameters = []

        for node in sorted(self.model.nodes()):
            cpd = self.estimate_cpd(node)
            parameters.append(cpd)

        return parameters