Particle learning for Bayesian semi-parametric stochastic volatility model
设计了一种序贯蒙特卡洛算法（粒子学习）来估计金融数据的贝叶斯半参数随机波动率模型，能在线更新后验分布并进行模型比较，模拟和真实数据验证了其与MCMC几乎一致的性能。
This article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.