  To understand the long-run behavior of Markov population models, the
computation of the stationary distribution is often a crucial part. We propose
a truncation-based approximation that employs a state-space lumping scheme,
aggregating states in a grid structure. The resulting approximate stationary
distribution is used to iteratively refine relevant and truncate irrelevant
parts of the state-space. This way, the algorithm learns a well-justified
finite-state projection tailored to the stationary behavior. We demonstrate the
method's applicability to a wide range of non-linear problems with complex
stationary behaviors.
