  We revisit the symbolic verification of Markov chains with respect to finite
horizon reachability properties. The prevalent approach iteratively computes
step-bounded state reachability probabilities. By contrast, recent advances in
probabilistic inference suggest symbolically representing all horizon-length
paths through the Markov chain. We ask whether this perspective advances the
state-of-the-art in probabilistic model checking. First, we formally describe
both approaches in order to highlight their key differences. Then, using these
insights we develop Rubicon, a tool that transpiles Prism models to the
probabilistic inference tool Dice. Finally, we demonstrate better scalability
compared to probabilistic model checkers on selected benchmarks. All together,
our results suggest that probabilistic inference is a valuable addition to the
probabilistic model checking portfolio -- with Rubicon as a first step towards
integrating both perspectives.
