R中非线性和非高斯状态空间模型的贝叶斯推理

R J. Pub Date : 2021-01-21 DOI:10.32614/RJ-2021-103
Jouni Helske, M. Vihola
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引用次数: 6

摘要

我们提出了一个R包bssm用于贝叶斯非线性/非高斯状态空间建模。与现有的软件包不同,bssm允许基于高斯近似(如拉普拉斯近似和扩展卡尔曼滤波器)的易于使用的近似推理。包也容纳离散观察潜在扩散过程。该推理基于超参数的全自动自适应马尔可夫链蒙特卡罗(MCMC),并具有可选的重要性采样后校正以消除任何近似偏差。该包还使用中间近似实现了直接伪边际MCMC和延迟接受伪边际MCMC。该软件包提供了一个易于使用的界面来定义具有非高斯观测模型的线性-高斯状态动力学模型,并具有用于指定自定义非线性和扩散模型的Rcpp接口。模型是分析各种时间序列数据的灵活工具。在这里,我们介绍了R包bssm,用于对具有几种可选MCMC采样策略的大型模型进行完全贝叶斯状态空间建模。该包的所有计算密集型部分都是
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bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package accommodates also discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package implements also a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers an easy-to-use interface to define models with linear-Gaussian state dynamics with non-Gaussian observation models, and has an Rcpp interface for specifying custom non-linear and diffusion models. models are a flexible tool for analysing a variety of time series data. Here we introduced the R package bssm for fully Bayesian state space modelling for a large class of models with several alternative MCMC sampling strategies. All computationally intensive parts of the package are
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