Bayesian inference for diffusion driven mixed-effects models
Bayesian inference for diffusion driven mixed-effects models
复制标题
扩散驱动混合效应模型的贝叶斯推理
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
C. Sherlock
中科院分区：
文献类型：
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作者：
G. Whitaker;A. Golightly;R. Boys;C. Sherlock
随机微分方程（SDEs）为模拟许多连续时间物理过程中固有的内在随机性提供了一个自然的框架。当在多个个体或实验单位中观察到这些过程时，随机驱动的混合效应模型允许量化个体之间（以及个体内）的变异。使用可能不完整且受测量误差影响的离散时间数据对此类模型进行贝叶斯推断是一个具有挑战性的问题，也是本文的重点。我们扩展了最近提出的MCMC计划，包括可编程驱动的混合效应框架。我们的方法的基础是开发一种新的结构，允许有效的采样条件的SDES，可能会表现出非线性动态之间的观察时间。我们将由此产生的计划，从一个简单的橙子树生长的模型，和真实的数据组成的蚜虫数量的各种不同的治疗方案下记录的观察所产生的合成数据。此外，我们提供了一个系统的比较，我们的方法与推理计划的基础上一个易于处理的近似的噪声，即线性噪声近似。
Stochastic differential equations (SDEs) provide a natural framework for modelling intrinsic stochasticity inherent in many continuous-time physical processes. When such processes are observed in multiple individuals or experimental units, SDE driven mixed-effects models allow the quantification of between (as well as within) individual variation. Performing Bayesian inference for such models, using discrete time data that may be incomplete and subject to measurement error is a challenging problem and is the focus of this paper. We extend a recently proposed MCMC scheme to include the SDE driven mixed-effects framework. Fundamental to our approach is the development of a novel construct that allows for efficient sampling of conditioned SDEs that may exhibit nonlinear dynamics between observation times. We apply the resulting scheme to synthetic data generated from a simple SDE model of orange tree growth, and real data consisting of observations on aphid numbers recorded under a variety of different treatment regimes. In addition, we provide a systematic comparison of our approach with an inference scheme based on a tractable approximation of the SDE, that is, the linear noise approximation.
扩散的数据增强
影响因子：
2.4
作者：
Papaspiliopoulos O
通讯作者：
Papaspiliopoulos O
用于扩散精确推理的马尔可夫链蒙特卡罗
影响因子：
1
作者：
SERMAIDIS G
通讯作者：
SERMAIDIS G