Bayesian estimation of parameters of a damped sinusoidal model by a Markov chain Monte Carlo method

被引：10

作者：

Barone, P ^{[1
]}

Ragona, R ^{[1
]}

机构：

[1] TELECOM ITALIA MOBILE, ROME, ITALY

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 1997年 / 45卷 / 07期

关键词：

D O I：

10.1109/78.599950

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A dynamic Monte Carlo method is proposed to compute the posterior means and covariances of the parameters of a damped sinusoidal model when an informative prior distribution is known. The Bayesian framework provides a sound mathematical ground, which possibly allows cane to overcome the approximations commonly used to cope with this difficult problem. Some simulations results are provided, which support the conclusion that the prior information can also be significantly improved when the data have a low signal-to-noise ratio.

引用

页码：1806 / 1814

页数：9

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