Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation: II
- 1 April 1996
- journal article
- Published by IOP Publishing in Inverse Problems
- Vol. 12 (2) , 121-137
- https://doi.org/10.1088/0266-5611/12/2/002
Abstract
Based on the theory of Bayesian inference and Gibbs sampling presented in part I of this series, the same numerical approach is applied to some more complicated models and conditions, such as periodic but non-harmonic signals, signals with decay, and signals with chirp, in spectral analysis and parameter estimation. Results demonstrate that even under these complicated conditions Bayesian inference and Gibbs sampling can still give very accurate results. It is also demonstrated that through the use of Bayesian inference methods it is possible to choose the most probable model based on known prior information and data, assuming a model space. Two model selection examples are presented which demonstrated the reliability of this approach.Keywords
This publication has 5 references indexed in Scilit:
- Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation. IInverse Problems, 1995
- Bayesian inference theory applied to hyperfine parameter distribution extraction in Mössbauer spectroscopyNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 1995
- Bayesian analysis. I. Parameter estimation using quadrature NMR modelsJournal of Magnetic Resonance (1969), 1990
- Parameter estimation of chirp signalsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984