条件化学反应网络的引导模拟。

IF 0.7 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes Pub Date : 2025-01-01 Epub Date: 2025-05-17 DOI:10.1007/s11203-025-09326-9

Marc Corstanje, Frank van der Meulen

{"title":"条件化学反应网络的引导模拟。","authors":"Marc Corstanje, Frank van der Meulen","doi":"10.1007/s11203-025-09326-9","DOIUrl":null,"url":null,"abstract":"Let X be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times <math><mrow><mn>0</mn> <mo><</mo> <msub><mi>t</mi> <mn>1</mn></msub> <mo><</mo> <mo>⋯</mo> <mo><</mo> <msub><mi>t</mi> <mi>n</mi></msub> </mrow> </math> , linear combinations <math> <mrow><msub><mi>v</mi> <mi>i</mi></msub> <mo>=</mo> <msub><mi>L</mi> <mi>i</mi></msub> <mi>X</mi> <mrow><mo>(</mo> <msub><mi>t</mi> <mi>i</mi></msub> <mo>)</mo></mrow> <mo>,</mo> <mspace></mspace> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mi>n</mi></mrow> </math> are observed for given matrices <math><msub><mi>L</mi> <mi>i</mi></msub> </math> . We show how the process that is conditioned on hitting the states <math> <mrow><msub><mi>v</mi> <mn>1</mn></msub> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <msub><mi>v</mi> <mi>n</mi></msub> </mrow> </math> 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.","PeriodicalId":43294,"journal":{"name":"Statistical Inference for Stochastic Processes","volume":"28 2","pages":"8"},"PeriodicalIF":0.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12101079/pdf/","citationCount":"0","resultStr":"{\"title\":\"Guided simulation of conditioned chemical reaction networks.\",\"authors\":\"Marc Corstanje, Frank van der Meulen\",\"doi\":\"10.1007/s11203-025-09326-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Let X be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times <math><mrow><mn>0</mn> <mo><</mo> <msub><mi>t</mi> <mn>1</mn></msub> <mo><</mo> <mo>⋯</mo> <mo><</mo> <msub><mi>t</mi> <mi>n</mi></msub> </mrow> </math> , linear combinations <math> <mrow><msub><mi>v</mi> <mi>i</mi></msub> <mo>=</mo> <msub><mi>L</mi> <mi>i</mi></msub> <mi>X</mi> <mrow><mo>(</mo> <msub><mi>t</mi> <mi>i</mi></msub> <mo>)</mo></mrow> <mo>,</mo> <mspace></mspace> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mi>n</mi></mrow> </math> are observed for given matrices <math><msub><mi>L</mi> <mi>i</mi></msub> </math> . We show how the process that is conditioned on hitting the states <math> <mrow><msub><mi>v</mi> <mn>1</mn></msub> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <msub><mi>v</mi> <mi>n</mi></msub> </mrow> </math> 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.\",\"PeriodicalId\":43294,\"journal\":{\"name\":\"Statistical Inference for Stochastic Processes\",\"volume\":\"28 2\",\"pages\":\"8\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12101079/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Inference for Stochastic Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s11203-025-09326-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Inference for Stochastic Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11203-025-09326-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

摘要

设X为一个化学反应过程，建模为一个多维连续时间跳跃过程。假设在给定的0 t 1⋯t n时刻，对于给定的矩阵L i，观察到线性组合v i = L i X (ti), i = 1,⋯n。我们展示了以达到状态v1，⋯v n为条件的过程是如何通过对无条件过程定律的测度变化而获得的。这就产生了一种从条件过程中获得加权样本的算法。数值模拟表明了我们的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Guided simulation of conditioned chemical reaction networks.

Let X be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times $0 < t_{1} < \dots < t_{n}$ , 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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Statistical Inference for Stochastic Processes STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

12.50%

发文量

期刊介绍： Statistical Inference for Stochastic Processes aims to publish high quality papers devoted to inference in either discrete or continuous time stochastic processes. This includes topics such as ARMA processes, GARCH processes and other time series models, as well as diffusion type processes, point processes, random fields and Markov processes. Papers related to spatial models and empirical processes are also within the scope of the journal. Special focus is placed on methodological advances and sound theoretical results, but submissions that expose potential applications of the developed theory to finance, insurance, economics, biology, physics and engineering are very much encouraged. Officially cited as: Stat Inference Stoch Process