Variational Bayes for generalized autoregressive models
Variational Bayes for generalized autoregressive models
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DOI：
10.1109/tsp.2002.801921
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发表时间：
2002-09
期刊：
影响因子：
--
通讯作者：
S. Roberts;W. Penny
中科院分区：
文献类型：
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作者：
S. Roberts;W. Penny
本文描述了一种广义自回归（GAR）模型的变分贝叶斯学习算法。噪声被建模为高斯分布的混合，而不是通常的单一高斯分布。这允许不同的数据点与不同的噪声水平相关联，并有效地提供AR系数的鲁棒估计。使用VB框架防止过拟合，并提供了AR阶和噪声模型阶的模型阶选择标准。我们证明了对于高斯噪声和无信息先验的噪声和权值精度的特殊情况，VB框架可以简化为贝叶斯证据框架。将该算法应用于合成数据和实际数据，取得了令人满意的效果。
We describe a variational Bayes (VB) learning algorithm for generalized autoregressive (GAR) models. The noise is modeled as a mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides robust estimation of AR coefficients. The VB framework is used to prevent overfitting and provides model-order selection criteria both for AR order and noise model order. We show that for the special case of Gaussian noise and uninformative priors on the noise and weight precisions, the VB framework reduces to the Bayesian evidence framework. The algorithm is applied to synthetic and real data with encouraging results.