Bayesian inference for nonlinear multivariate diffusion models observed with error
Bayesian inference for nonlinear multivariate diffusion models observed with error
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DOI：
10.1016/j.csda.2007.05.019
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发表时间：
2008-01-01
影响因子：
1.8
通讯作者：
Wilkinson, D. J.
中科院分区：
文献类型：
--
作者：
Golightly, A.;Wilkinson, D. J.
由随机微分方程（SDES）控制的扩散过程是一个成熟的工具，用于模拟来自广泛领域的连续时间数据。因此，技术已经发展到估计部分和离散观测扩散参数。基于可能性的推断可能是有问题的，因为封闭形式的转换密度很少可用。一个广泛使用的解决方案涉及在每对观测之间引入潜在数据点，以允许真实跃迁密度的欧拉-丸山近似变得准确。在最近的文献中，马尔可夫链蒙特卡罗（MCMC）方法已被用来采样的后验分布的潜在数据和模型参数，然而，天真的计划遭受的混合问题，与程度的增强。一个全球MCMC计划，可以适用于一大类的扩散，其性能不会受到不利影响的潜在值的数量，因此探索。该方法说明了通过估计参数的自动调节基因网络，使用部分和离散的数据，受到测量误差。(C)2007 Elsevier B.V.保留所有权利。
Diffusion processes governed by stochastic differential equations (SDEs) are a well-established tool for modelling continuous time data from a wide range of areas. Consequently, techniques have been developed to estimate diffusion parameters from partial and discrete observations. Likelihood-based inference can be problematic as closed form transition densities are rarely available. One widely used solution involves the introduction of latent data points between every pair of observations to allow a Euler-Maruyama approximation of the true transition densities to become accurate. In recent literature, Markov chain Monte Carlo (MCMC) methods have been used to sample the posterior distribution of latent data and model parameters; however, naive schemes suffer from a mixing problem that worsens with the degree of augmentation. A global MCMC scheme that can be applied to a large class of diffusions and whose performance is not adversely affected by the number of latent values is therefore explored. The methodology is illustrated by estimating parameters governing an auto-regulatory gene network, using partial and discrete data that are subject to measurement error. (C) 2007 Elsevier B.V. All rights reserved.