自适应核密度估计在引力波数据分析中的应用
Adaptive Kernel Density Estimation proposal in gravitational wave data analysis
摘要：
马尔可夫链蒙特卡罗方法是贝叶斯框架中常用的目标后验分布的采样方法。它的效率在很大程度上取决于用于构建链的建议。最好的跳跃建议是一个非常相似的未知目标分布，因此，我们提出了一个自适应的建议核密度估计（KDE）的基础上。我们根据模型参数的相关性对它们进行分组，并根据每组已经接受的点构建KDE。我们调整基于KDE的提案，直到它稳定下来。我们认为，这样的建议可能是有帮助的应用程序中的数据量正在增加，并在超模型采样。我们在几个天体物理数据集（IPTA和LISA）上测试了它，并表明在某些情况下，基于KDE的建议也有助于减少链的自相关长度。在大量参数之间的强相关性的情况下，该建议的效率降低。
英文摘要：
Markov Chain Monte Carlo approach is frequently used within Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on the proposal used to build the chain. The best jump proposal is the one that closely resembles the unknown target distribution, therefore we suggest an adaptive proposal based on Kernel Density Estimation (KDE). We group parameters of the model according to their correlation and build KDE based on the already accepted points for each group. We adapt the KDE-based proposal until it stabilizes. We argue that such a proposal could be helpful in applications where the data volume is increasing and in the hyper-model sampling. We tested it on several astrophysical datasets (IPTA and LISA) and have shown that in some cases KDE-based proposal also helps to reduce the autocorrelation length of the chains. The efficiency of this proposal is reduces in case of the strong correlations between a large group of parameters.
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。
