Learning tapestries：一种用于带误差测量的开放混沌系统的统计学习基底
Learning tapestries, a statistical learning substrate for open chaotic systems measured with error
AI总结：
针对带误差测量的开放混沌系统统计推断难题，提出将测量数据表示为合理伪未来历史线程集合的learning tapestries方法，可实现渐近一致预测，具备变量选择、贝叶斯更新等功能，并经气候数据验证有效。
摘要：
带误差测量的开放混沌系统的统计推断问题，因混沌带来的不确定性与各类随机或外部变异源的相互作用而变得复杂。本文提出一种方法，将受误差影响的大型开放混沌系统的测量数据表示为合理伪未来历史的线程集合，以支持统计分析。该表示可提供渐近一致的预测分布，用于构建预测似然方法，其功能包括：1. 提供变量选择框架；2. 提供贝叶斯更新框架，例如当测量到提前3季的数据时，提前4季的预测可自然学习更新；3. 允许沿未来历史检查条件场景，用于规划目的；4. 可对具有更高信噪比的变量-延迟组合进行排序。通过检验该方法在预测9年共4个季节的气候变量（包括两个地点的局部气温和降雨量测量值，最多提前4季预测）中的表现，对其学习和变量选择性能进行了测试。
英文摘要：
The problem of statistical inference for open chaotic systems measured with error is complicated by the interaction of the uncertainty introduced by chaos, and the various sources of random or external variation. Here a method of representing measured data from large open chaotic systems subject to error as collections of threads of plausible pseudo future histories to enable statistical analysis is described. This representation provides asymptotically consistent predictive distributions, for use in developing predictive likelihood methods which: 1. provide a framework for variable selection, 2. provide a framework for Bayesian updating, so for example 4 season ahead predictions learn naturally as the 3rd season ahead is measured. 3. allows examination of conditional scenarios along the future histories for planning purposes. 4. allows the ranking of variable, delay combinations with higher signal to noise ratio. The method is tested for learning and variable selection by examining its behavior in predicting 9 years across 4 seasons of climate variables, including local temperature and rainfall measurements at two locations, predicting up to 4 seasons ahead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
