  Rare properties remain a challenge for statistical model checking (SMC) due
to the quadratic scaling of variance with rarity. We address this with a
variance reduction framework based on lightweight importance splitting
observers. These expose the model-property automaton to allow the construction
of score functions for high performance algorithms.
  The confidence intervals defined for importance splitting make it appealing
for SMC, but optimising its performance in the standard way makes distribution
inefficient. We show how it is possible to achieve equivalently good results in
less time by distributing simpler algorithms. We first explore the challenges
posed by importance splitting and present an algorithm optimised for
distribution. We then define a specific bounded time logic that is compiled
into memory-efficient observers to monitor executions. Finally, we demonstrate
our framework on a number of challenging case studies.
