Taming Reasoning in Temporal Probabilistic Relational Models
  Evidence often grounds temporal probabilistic relational models over time,
which makes reasoning infeasible. To counteract groundings over time and to
keep reasoning polynomial by restoring a lifted representation, we present
temporal approximate merging (TAMe), which incorporates (i) clustering for
grouping submodels as well as (ii) statistical significance checks to test the
fitness of the clustering outcome. In exchange for faster runtimes, TAMe
introduces a bounded error that becomes negligible over time. Empirical results
show that TAMe significantly improves the runtime performance of inference,
while keeping errors small.
