Returns a copy of the distribution.

        Returns
        -------
        LinearGaussianCPD: copy of the distribution

        Examples
        --------
        >>> from pgmpy.factors.continuous import LinearGaussianCPD
        >>> cpd = LinearGaussianCPD('Y',  [0.2, -2, 3, 7], 9.6, ['X1', 'X2', 'X3'])
        >>> copy_cpd = cpd.copy()
        >>> copy_cpd.variable
        'Y'
        >>> copy_cpd.evidence
        ['X1', 'X2', 'X3']

def copy(self):
        """
        Returns a copy of the distribution.

        Returns
        -------
        LinearGaussianCPD: copy of the distribution

        Examples
        --------
        >>> from pgmpy.factors.continuous import LinearGaussianCPD
        >>> cpd = LinearGaussianCPD('Y',  [0.2, -2, 3, 7], 9.6, ['X1', 'X2', 'X3'])
        >>> copy_cpd = cpd.copy()
        >>> copy_cpd.variable
        'Y'
        >>> copy_cpd.evidence
        ['X1', 'X2', 'X3']
        """
        copy_cpd = LinearGaussianCPD(self.variable, self.beta, self.variance,
                                     list(self.evidence))

        return copy_cpd