Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation .2.
Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation .2.
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DOI：
10.1088/0266-5611/12/2/002
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发表时间：
1996-04-01
期刊：
影响因子：
2.1
通讯作者：
Hodgson, RJW
中科院分区：
文献类型：
--
作者：
Dou, LX;Hodgson, RJW
基于本系列第一部分中介绍的贝叶斯推理和吉布斯采样理论，同样的数值方法应用于一些更复杂的模型和条件，如周期非谐波信号、有衰减的信号和有啁啾的信号，在频谱分析和参数估计中。结果表明，即使在这些复杂的条件下，贝叶斯推理和吉布斯抽样仍然可以给出非常准确的结果。通过使用贝叶斯推理方法，可以根据已知的先验信息和数据，假设一个模型空间，选择最可能的模型。给出了两个模型选择实例，验证了该方法的可靠性。
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.