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Arguing about the equilibrium distribution of continuous-time Markov chainscan be vital for showing properties about the underlying systems. For examplein biological systems, bistability of a chemical reaction network can hint atits function as a biological switch. Unfortunately, the state space of thesesystems is infinite in most cases, preventing the use of traditional steadystate solution techniques. In this paper we develop a new approach to tacklethis problem by first retrieving geometric bounds enclosing a major part of thesteady state probability mass, followed by a more detailed analysis revealingstate-wise bounds.

Bounding the Equilibrium Distribution of Markov Population Models