论文标题
粒子吉布斯方法及其扩展和变体的注释
A Note on Particle Gibbs Method and its Extensions and Variants
论文作者
论文摘要
国家空间模型的高维状态轨迹对贝叶斯推论构成了挑战。粒子吉布斯（PG）方法已被广泛用于从状态空间模型的后部采样。基本上，粒子吉布斯是一种粒子马尔可夫链蒙特卡洛（PMCMC）算法，该算法通过绘制模型参数和状态从其条件分布中模拟吉布斯采样器。 本教程提供了粒子吉布斯（PG）方法及其扩展和变体的介绍性视图，并通过非线性状态空间模型（SSM）中的一些推论示例来说明。我们还使用两种不同的编程语言实施PG采样器：Python和Rust。还为各种PG方法提供了Python和Rust程序的运行时间性能的比较。
High-dimensional state trajectories of state-space models pose challenges for Bayesian inference. Particle Gibbs (PG) methods have been widely used to sample from the posterior of a state space model. Basically, particle Gibbs is a Particle Markov Chain Monte Carlo (PMCMC) algorithm that mimics the Gibbs sampler by drawing model parameters and states from their conditional distributions. This tutorial provides an introductory view on Particle Gibbs (PG) method and its extensions and variants, and illustrates through several examples of inference in non-linear state space models (SSMs). We also implement PG Samplers in two different programming languages: Python and Rust. Comparison of run-time performance of Python and Rust programs are also provided for various PG methods.
