论文标题
引力波数据分析中的自适应核密度估计提案
Adaptive Kernel Density Estimation proposal in gravitational wave data analysis
论文作者
论文摘要
马尔可夫链蒙特卡洛方法经常在贝叶斯框架内使用，以对目标后部分布进行采样。它的效率在很大程度上取决于用于建造链条的建议。最好的跳跃提案是与未知目标分布非常相似的提案，因此我们建议基于内核密度估计（KDE）的自适应提案。我们根据模型的相关性将其分组，并根据每个组已经接受的点构建KDE。我们调整基于KDE的建议，直到稳定为止。我们认为，这样的建议在数据量增加和超模型采样的应用中可能有所帮助。我们在几个天体物理数据集（IPTA和LISA）上对其进行了测试，并表明在某些情况下，基于KDE的建议还有助于减少链的自相关长度。在大量参数之间存在很强的相关性的情况下，该提案的效率降低了。
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.
