Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
非线性非高斯状态空间模型的数值加速重要性抽样
We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal…