TIME SERIES ANALYSIS VIA MECHANISTIC MODELS
TIME SERIES ANALYSIS VIA MECHANISTIC MODELS
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DOI：
10.1214/08-aoas201
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发表时间：
2009-03-01
影响因子：
1.8
通讯作者：
King, Aaron A.
中科院分区：
文献类型：
--
作者：
Breto, Carles;He, Daihai;King, Aaron A.
通过机械模型进行时间序列分析的目的是将动态系统的已知或假设结构与随时间收集的观测结果相协调。我们开发了一个框架，用于构建非线性机械模型和进行推理。我们的框架允许考虑隐式动态模型，这意味着随机动态系统的统计模型，由模拟算法来生成样本路径。在隐式模型上操作的推理过程被称为具有即插即用属性。我们的工作建立在最近开发的部分观测马尔可夫模型的即插即用推理方法。我们介绍了一类隐式指定的马尔可夫链的随机转移率，我们证明了它的适用性，在生物系统的统计推断开放的问题。作为一个例子，这些模型显示，如果麻疹传播动力学的新视角。作为第二个例子，我们提出了一个霍乱发病率数据的机制分析，涉及两个竞争菌株的病原体霍乱弧菌之间的相互作用。
The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for Constructing nonlinear mechanistic models and carrying Out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give if fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.