Stochastic ordering tools for continuous-time Markov chains and applications to reaction network models
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| Format: | Preprint |
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2026
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| _version_ | 1866910093941932032 |
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| author | Cappelletti, Daniele Cuniberti, Giulio Siri, Paola |
| author_facet | Cappelletti, Daniele Cuniberti, Giulio Siri, Paola |
| contents | Stochastic reaction networks are mathematical models with a wide range of applications in biochemistry, ecology, and epidemiology, and are often complex to analyze. Except for some special cases, it is generally difficult to predict how the abundances of all considered species evolve over time. A possible approach to address this issue is to develop tools to compare the model under study with a similar one whose behavior is better understood. The main contribution of our work is to provide direct and computable conditions that can be used to ensure the existence of an ordered coupling between two stochastic reaction networks and to identify which parameter changes in a given model lead to an increase or decrease in the count of certain species. We also make available an algorithm that implements our theory, and we illustrate it with several applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_00756 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stochastic ordering tools for continuous-time Markov chains and applications to reaction network models Cappelletti, Daniele Cuniberti, Giulio Siri, Paola Probability Molecular Networks 60E15, 60J27, 60J28, 92C42 Stochastic reaction networks are mathematical models with a wide range of applications in biochemistry, ecology, and epidemiology, and are often complex to analyze. Except for some special cases, it is generally difficult to predict how the abundances of all considered species evolve over time. A possible approach to address this issue is to develop tools to compare the model under study with a similar one whose behavior is better understood. The main contribution of our work is to provide direct and computable conditions that can be used to ensure the existence of an ordered coupling between two stochastic reaction networks and to identify which parameter changes in a given model lead to an increase or decrease in the count of certain species. We also make available an algorithm that implements our theory, and we illustrate it with several applications. |
| title | Stochastic ordering tools for continuous-time Markov chains and applications to reaction network models |
| topic | Probability Molecular Networks 60E15, 60J27, 60J28, 92C42 |
| url | https://arxiv.org/abs/2604.00756 |