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首页> 外文期刊>Journal of machine learning research >Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points
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Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points

机译：大型图形模型的变化点计算：具有变化点的高斯图形模型的可扩展算法

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Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorize-minimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating process, we show that with high probability, the algorithm converges to a value that is within statistical error of the true change-point. A fast implementation of the algorithm using Markov Chain Monte Carlo is also introduced. The performances of the proposed algorithms are evaluated on synthetic data sets and the algorithm is also used to analyze structural changes in the S{&}P 500 over the period 2000-2016.
机译：具有变化点的图形模型是计算挑战，特别是在观察点数和图中节点数量大的情况下。 专注于高斯图形模型，我们介绍了一种近似大大最小化（MM）算法，可用于计算大图形模型中的变化点。 该算法的阶数比蛮力搜索快。 在数据生成过程的某些规律条件下，我们表明，具有高概率，算法会聚到真实变更点的统计错误中的值。 还介绍了使用马尔可夫链蒙特卡罗的算法的快速实现。 在合成数据集中评估所提出的算法的性能，并且该算法还用于分析2000-2016周期的S {＆} P 500中的结构变化。

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