基于随机松弛的吉布斯随机序列贝叶斯估计

Proceedings KORUS 2000. The 4th Korea-Russia International Symposium On Science and Technology Pub Date : 2000-06-27 DOI:10.1109/KORUS.2000.866016

V. Vasyukov, D. Goleshchikhin

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引用次数: 0

摘要

研究了Metropolis-Hastings随机松弛法应用于最优Gibbs随机信息插值的可能性。以条件高斯随机序列为例，以在高斯白噪声中观察到的吉布斯序列为消息模型。这样的模型被使用，例如用于语音信号描述。介绍了基本的Metropolis-Hastings算法，并给出了一些实验结果。

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Gibbs random sequences Bayes estimation based on stochastic relaxation

An opportunity of Metropolis-Hastings stochastic relaxation procedure application for optimal Gibbs random message interpolation is investigated. A conditional-Gaussian random sequence as an example of Gibbs sequence observed in white Gaussian noise is used as message model. Such models are used, e.g. for speech signal description. The basic Metropolis-Hastings algorithm is described and some experimental results are presented.

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Proceedings KORUS 2000. The 4th Korea-Russia International Symposium On Science and Technology

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