ABSTRACT
Chirp signals are frequently used in different areas of science and engineering. MCMC-based Bayesian inference is done here for the purpose of one-step and multiple-step prediction in the case of the one-dimensional single chirp signal with iid error structure as well as dependent error structure with exponentially decaying covariances. We use Gibbs sampling technique and random walk MCMC to update the parameters. We perform total five simulation studies for the illustration purpose. We also do some real-data analysis to show how the method is working in practice.
MATHEMATICS SUBJECT CLASSIFICATION:
Acknowledgments
The author is very grateful to Moumita Das for her insightful comments and suggestions which improve the article in every aspect. The author is also grateful to the assistance obtained from uci machine learning data repository (http://archive.ics.uci.edu/ml) for the real datasets. The author also cordially thanks two anonymous referees for their very valuable and important comments which help to improve the previous manuscript thoroughly.