Abstract
Filtering technology has been widely applied in signal processing and object tracking for decades, and most recently it has become a useful instrument for time series analysis. In this article, we introduce several popular technologies developed for analysis of the dynamic state space model, including Kalman and particle filtering, Markov chain Monte Carlo algorithms, as well as the sequential Bayesian learning method. Their applications in fields of interest are also discussed. Filtering technologies have great superiority in solving the problems arising from management and communications, making them deserving of further exploration.
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