论文标题
跳跃马尔可夫线性系统的贝叶斯参数识别
Bayesian Parameter Identification for Jump Markov Linear Systems
论文作者
论文摘要
本文提出了一种用于识别跳跃马尔可夫线性系统参数的贝叶斯方法。主要动机是在不依赖于数据长度参数中渐近的情况下对参数不确定性进行准确的量化。为了实现这一目标，本文详细介绍了一种粒子 - 基布斯采样方法，该方法可提供所需的后验分布中的样品。这些样品是通过使用修改的离散粒子滤波器和精心选择的共轭先验来产生的。
This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertainty without relying on asymptotic in data-length arguments. To achieve this, the paper details a particle-Gibbs sampling approach that provides samples from the desired posterior distribution. These samples are produced by utilising a modified discrete particle filter and carefully chosen conjugate priors.
