Guided simulation of conditioned chemical reaction networks
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916654784446464 |
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| author | Corstanje, Marc van der Meulen, Frank |
| author_facet | Corstanje, Marc van der Meulen, Frank |
| contents | Let $X$ be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times $0< t_1 < \cdots <t_n$, linear combinations $v_i = L_i X(t_i),\, i=1,\dots ,n$ are observed for given matrices $L_i$. We show how the process that is conditioned on hitting the states $v_1,\dots, v_n$ is obtained by a change of measure on the law of the unconditioned process. This results in an algorithm for obtaining weighted samples from the conditioned process. Our results are illustrated by numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04457 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Guided simulation of conditioned chemical reaction networks Corstanje, Marc van der Meulen, Frank Probability Computation 60J27, 60J28, 60J74 Let $X$ be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times $0< t_1 < \cdots <t_n$, linear combinations $v_i = L_i X(t_i),\, i=1,\dots ,n$ are observed for given matrices $L_i$. We show how the process that is conditioned on hitting the states $v_1,\dots, v_n$ is obtained by a change of measure on the law of the unconditioned process. This results in an algorithm for obtaining weighted samples from the conditioned process. Our results are illustrated by numerical simulations. |
| title | Guided simulation of conditioned chemical reaction networks |
| topic | Probability Computation 60J27, 60J28, 60J74 |
| url | https://arxiv.org/abs/2312.04457 |