  The unfolding of detector effects is a key aspect of comparing experimental
data with theoretical predictions. In recent years, different Machine-Learning
methods have been developed to provide novel features, e.g. high dimensionality
or a probabilistic single-event unfolding based on generative neural networks.
Traditionally, many analyses unfold detector effects using
transfer-matrix--based algorithms, which are well established in
low-dimensional unfolding. They yield an unfolded distribution of the total
spectrum, together with its covariance matrix. This paper proposes a method to
obtain probabilistic single-event unfolded distributions, together with their
uncertainties and correlations, for the transfer-matrix--based unfolding. The
algorithm is first validated on a toy model and then applied to pseudo-data for
the $pp\rightarrow Z\gamma \gamma$ process. In both examples the performance is
compared to the single-event unfolding of the Machine-Learning--based Iterative
cINN unfolding (IcINN).
