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 for the latent states based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package accommodates also discretised diffusion latent state 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 or a delayed acceptance pseudo-marginal MCMC using the approximations. The package supports directly models with linear-Gaussian state dynamics (but with non-Gaussian observation models), and has an Rcpp interface for specifying custom non-linear models.

翻译：我们为Bayesian非线性/非Gaussian州空间建模提供了R包 bssm。 与现有的包件不同, bsm允许基于Gaussian近似值(如Laplace近似值和扩展的Kalman过滤器)的潜在状态容易使用的近似推理。 包件还包含离散扩散潜伏状态过程。 推理基于超参数上的完全自动、 适应性的Markov链 Monte Carlo( MC MC ), 选择重要的取样后校准, 以消除任何近似偏差。 包件还安装了直接的伪边际 MC 或延迟接受的伪边际 MC 。 包件直接支持线性- Gaussian 州动态模型( 但与非伽西观察模型), 并有一个用于指定自定义非线性非线性模型的 Rcpp 界面 。