Gibbs ensembles for incompatible dependency networks.
Gibbs ensembles for incompatible dependency networks.
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DOI：
10.1002/wics.1273
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发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Wang, Yuchung J
中科院分区：
文献类型：
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作者：
Chen, Shyh-Huei;Ip, Edward H;Wang, Yuchung J
在大多数统计应用中，吉布斯采样器是关于兼容的条件指定分布的推断的选择方法。相容性确保存在唯一的吉布斯分布。对于依赖网络等复杂模型的机器学习，条件模型有时是不兼容的。在本文中，我们回顾了集成方法使用吉布斯采样器作为基本程序。吉布斯系综由同一条件模型的不同扫描顺序产生的许多联合分布组成，并且解是系综的加权和。该算法是可扩展的，可以处理大规模的高维数据集。所提出的方法提供了联合分布，符合条件的规格比线性规划和固定扫描吉布斯采样器单独获得的解决方案。由于不相容性，吉布斯采样器的不变分布依赖于扫描顺序。吉布斯系综是从不同扫描顺序的吉布斯样本估计的联合分布的集合。
In most statistical applications, the Gibbs sampler is the method of choice for inference regarding conditionally specified distributions that are compatible. Compatibility ensures that a unique Gibbs distribution exists. For machine learning of complex models such as dependency networks, the conditional models are sometimes incompatible. In this paper, we review an ensemble approach using the Gibbs sampler as the base procedure. A Gibbs ensemble consists of many joint distributions resulting from different scan orders of the same conditional model, and the solution is a weighted sum of the ensemble. The algorithm is scalable and can handle large data sets of high dimensionality. The proposed approach provides joint distributions that conform with the conditional specifications better than the solutions obtained by linear programming and by a fixed-scan Gibbs sampler alone. Owing to incompatibility, the invariant distribution of a Gibbs sampler is scan-order dependent. A Gibbs ensemble is the collection of joint distributions estimated from the Gibbs samples of different scan orders.