Impute values and confidence of the value from the predictive
        distribution of the given latent state.

        :param Y: A list of constraints to apply when sampling.  Each constraint
            is a triplet of (r, d, v): r is the row index, d is the column
            index and v is the value of the constraint
        :type Y: list of lists
        :param Q: A list of values to sample.  Each value is doublet of (r, d):
            r is the row index, d is the column index
        :type Q: list of lists
        :param n: the number of samples to use in the imputation
        :type n: int

        :returns: list of lists -- list of (value, confidence) tuples in the
            same order as specified by Q

def impute_and_confidence(self, M_c, X_L, X_D, Y, Q, seed, n):
        """Impute values and confidence of the value from the predictive
        distribution of the given latent state.

        :param Y: A list of constraints to apply when sampling.  Each constraint
            is a triplet of (r, d, v): r is the row index, d is the column
            index and v is the value of the constraint
        :type Y: list of lists
        :param Q: A list of values to sample.  Each value is doublet of (r, d):
            r is the row index, d is the column index
        :type Q: list of lists
        :param n: the number of samples to use in the imputation
        :type n: int

        :returns: list of lists -- list of (value, confidence) tuples in the
            same order as specified by Q
        """
        get_next_seed = make_get_next_seed(seed)
        if isinstance(X_L, (list, tuple)):
            assert isinstance(X_D, (list, tuple))
            # TODO: multistate impute doesn't exist yet
            # e,confidence = su.impute_and_confidence_multistate(
            #   M_c, X_L, X_D, Y, Q, n, self.get_next_seed)
            e, confidence = su.impute_and_confidence(
                M_c, X_L, X_D, Y, Q, n, get_next_seed)
        else:
            e, confidence = su.impute_and_confidence(
                M_c, X_L, X_D, Y, Q, n, get_next_seed)
        return (e, confidence)