递推的贝叶斯估计方法

A Survey of Recursive Bayesian Estimation Methods

对贝叶斯估计的原理及应用进行了综述,在系统阐述贝叶斯估计理论的基础上,按照对后验概率密度函数表示方式的不同,分析和总结了隐马尔可夫模型、卡尔曼滤波、分布拟合滤波以及粒子滤波等算法的特点、使用方法和使用范围;最后,对贝叶斯估计的发展方向进行了展望。

The theory and applications related to sequential Bayesian estimation were surveyed. Various estimating algorithms, such as the Hidden Markov Model, the Kalman Filter, the Assumed-density Filter and the Particle Filter were analyzed and summarized according to the way their posterior density function are expressed. Finally, further research directions are pointed out.