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arXiv 1601.01972cond-mat.stat-mechmath.STphysics.data-anq-bio.QMstat.MLstat.TH
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随机反应扩散过程的Cox过程表示与推断

Cox process representation and inference for stochastic reaction-diffusion processes

David Schnoerr, Ramon Grima, Guido Sanguinetti

摘要:

在许多系统中,复杂的行为来自于空间分布的粒子或代理的随机相互作用。随机反应扩散过程被广泛用于从生物学到社会科学等学科的行为建模,但众所周知,它们很难模拟和校准观测数据。在这里,我们使用统计物理学和机器学习的思想来解决从数据中学习随机反应扩散过程的逆问题。我们的解决方案依赖于随机反应扩散过程和时空Cox过程之间的非平凡连接,这是计算统计中一类研究得很好的模型。这种连接导致一个有效的和灵活的算法参数推断和模型选择。我们的方法显示了良好的准确性,从系统生物学和流行病学的数字和真实数据的例子。我们的工作既提供了对时空随机系统的见解,也为计算建模中的一个长期存在的问题提供了一个实用的解决方案。

英文摘要:

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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