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.

Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models

Bortolussi L.;
2021-01-01

Abstract

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.
2021
978-3-030-85171-2
978-3-030-85172-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2998693
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