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首页> 外文期刊>Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America >Adaptive mixture modeling metropolis methods for bayesian analysis of nonlinear state-space models
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Adaptive mixture modeling metropolis methods for bayesian analysis of nonlinear state-space models

机译：非线性状态空间模型贝叶斯分析的自适应混合建模大都市方法

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摘要

We describe a strategy for Markov chain Monte Carlo analysis of nonlinear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis- Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the nonlinearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters.We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.
机译：我们描述了一种用于非线性，非高斯状态空间模型的马尔可夫链蒙特卡洛分析的策略，其中涉及对动态，潜在状态变量和固定模型参数进行推断的批处理分析。关键的创新是用于状态变量时间序列的Metropolis-Hastings方法，该方法基于使用普通混合物的滤波和平滑密度的顺序逼近。这些混合物使用精确的局部混合物逼近方法通过非线性传播，我们使用再生程序来处理混合物组分的潜在简并性。这为顺序滤波和追溯平滑分布提供了准确，直接的近似值，从而为构造状态集的后验对象提供了有用的全局Metropolis建议分布的构造。该分析被嵌入到Gibbs采样器中，以包含不确定的固定参数。我们给出了一个受系统生物学应用启发的示例。补充材料提供了一个基于随机波动率模型和MATLAB代码的示例。

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