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Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

机译:贝叶斯P样条和R中的高级计算用于时空点过程的变化点分析

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This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.
机译:这项工作提出了时空点过程数据的变化点检测的一种新方法的高级计算方面。我们在建立贝叶斯数据分层模型并声明变更点数量和位置的先验猜想的基础上,对方法进行了总结,并展示了如何做出有关接受潜在变更点的决定。这项工作的重点是选择一种方法,该方法可以检测正确的变更点并在可行的计算时间内提供可靠的平滑估计。我们建议在计算和模型拟合性能的角度下,将贝叶斯P样条作为管理空间变化的合适工具。概述了主要的计算挑战,并提出了一种涉及R中并行计算的解决方案,并在仿真研究中对其进行了测试。本文还介绍了过去20年意大利地震事件的数据集。

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