Applying Bayesian Inference for Signal Extraction Model of Time Series

by Jae J. Lee .

Abstract: 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.

Key Words: Hyperparameters, Bayesian Inference, Importance Sampling, Signal Extraction, Time Series Analysis

Author:
Jae J. Lee , leej@newpaltz.edu

Editor: Abdulnasser Hatemi-J , abdulnasser.hatemi-j@ihh.hj.se

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