Sequential Bayesian Inference

Sequential Bayesian Inference, also known as recursive Bayesian estimation, is a statistical inference method performed in the context of time series, aimed at real-time updating and estimation of hidden state space models. This method progressively refines the posterior distribution of model parameters by continuously incorporating new observational data, using Bayes' theorem, thereby achieving dynamic inference of the system state. It holds significant application value in fields such as signal processing, robotic navigation, financial forecasting, and more.