Bayesian estimation of state-space models applied to deconvolution of Bernoulli-Gaussian processes

被引：33
|
作者：
Doucet, A
Duvaut, P
机构：
[1] CEN Saclay LETI/DEIN/SPE, Bâtiment 451
[2] ETIS - ENSEA Groupe Signal
来源：
SIGNAL PROCESSING | 1997年 / 57卷 / 02期
关键词：
Bayesian estimation; Markov chain Monte Carlo methods; Gibbs sampler; state-space models; Bernoulli-Gaussian processes; deconvolution;
D O I：
10.1016/S0165-1684(96)00192-2
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：
In this article, we use stochastic simulation techniques to estimate the a posteriori density of the hidden-state process and hyperparameters of conditionally linear Gaussian state-space models. The estimation method relies on Markov chains Monte Carlo methods and more especially the Gibbs sampler. We apply this algorithm to non-blind and blind Bayesian deconvolution of Bernoulli-Gaussian processes. Convergence of the algorithm is established. In simulations, very satisfactory results are obtained. (C) 1997 Elsevier Science B.V.
引用
收藏
页码：147 / 161
页数：15
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