Particle Gibbs for Likelihood-Free Inference of State Space Models with Application to Stochastic Volatility
用于状态空间模型无似然推断的粒子 Gibbs 方法及随机波动率应用
State space models (SSMs) are widely used to describe dynamic systems. However, when the likelihood of the observations is intractable, parameter inference for SSMs cannot be easily carried out using standard Markov chain Monte Carlo or sequential Monte Carlo methods. In this paper, we propose a particle Gibbs sampler as a general strategy to handle SSMs with intractable likelihoods in the approximate Bayesian computation (ABC) setting. The proposed sampler incorporates a…