Applying Bayesian Inference for Signal Extraction Model of Time Series
by Jae J. Lee
This paper explores a way to apply Bayesian Inference to Signal Extraction model of time series. The basic idea is to treat hyperparameters in stochastic time series models of the components as a random vector and compute a posterior density function using Bayesian inference. Then Importance Sampling as a variance reduction technique is applied to integrate out the hyperparameters. A real time series data are used to explain how to apply this concept.
Hyperparameters, Bayesian Inference, Importance Sampling, Signal Extraction, Time Series Analysis
Jae J. Lee
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