  We use results from zero-error information theory to determine the set of
non-injective functions through which a Markov chain can be projected without
losing information. These lumping functions can be found by clique partitioning
of a graph related to the Markov chain. Lossless lumping is made possible by
exploiting the (sufficiently sparse) temporal structure of the Markov chain.
Eliminating edges in the transition graph of the Markov chain trades the
required output alphabet size versus information loss, for which we present
bounds.
