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A new variational Bayesian algorithm with application to human mobility pattern modeling

机译：一种新的变分贝叶斯算法及其在人员流动模式建模中的应用

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摘要

A new variational Bayesian (VB) algorithm, split and eliminate VB (SEVB), for modeling data via a Gaussian mixture model (GMM) is developed. This new algorithm makes use of component splitting in a way that is more appropriate for analyzing a large number of highly heterogeneous spiky spatial patterns with weak prior information than existing VB-based approaches. SEVB is a highly computationally efficient approach to Bayesian inference and like any VB-based algorithm it can perform model selection and parameter value estimation simultaneously. A significant feature of our algorithm is that the fitted number of components is not limited by the initial proposal giving increased modeling flexibility. We introduce two types of split operation in addition to proposing a new goodness-of-fit measure for evaluating mixture models. We evaluate their usefulness through empirical studies. In addition, we illustrate the utility of our new approach in an application on modeling human mobility patterns. This application involves large volumes of highly heterogeneous spiky data; it is difficult to model this type of data well using the standard VB approach as it is too restrictive and lacking in the flexibility required. Empirical results suggest that our algorithm has also improved upon the goodness-of-fit that would have been achieved using the standard VB method, and that it is also more robust to various initialization settings.
机译：开发了一种新的变分贝叶斯（VB）算法，即拆分和消除VB（SEVB），用于通过高斯混合模型（GMM）建模数据。与现有的基于VB的方法相比，这种新算法以一种更适合于分析大量具有较弱先验信息的高度异构尖峰空间模式的方式利用组件分割。 SEVB是一种用于贝叶斯推理的高效计算方法，并且像任何基于VB的算法一样，它可以同时执行模型选择和参数值估计。我们算法的一个重要特征是组件的装配数量不受初始提议的限制，从而增加了建模的灵活性。除了提出一种用于评估混合模型的拟合优度度量外，我们还介绍了两种拆分操作。我们通过实证研究评估其有效性。此外，我们还演示了我们的新方法在模拟人类出行模式的应用中的实用性。该应用程序涉及大量高度异构的尖峰数据。使用标准VB方法很难很好地对这种类型的数据进行建模，因为它过于严格且缺乏所需的灵活性。经验结果表明，我们的算法还改善了使用标准VB方法获得的拟合优度，并且它对各种初始化设置也更加健壮。

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