论文标题
基于巴特的泊松过程推断
BART-based inference for Poisson processes
论文作者
论文摘要
贝叶斯加性回归树（BART）的有效性已在多种情况下得到证明，包括非参数回归和分类。引入了用于估计不均匀泊松过程强度的BART方案。在各种应用中，包括医学成像，天体物理学和网络流量分析，泊松强度估计是至关重要的任务。新方法可以在非参数回归设置中对强度的全面推断。通过对合成和实际数据集的仿真研究，将新方案的性能与五个维度进行了证明，并将新方案与替代方法进行了比较。
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification. A BART scheme for estimating the intensity of inhomogeneous Poisson processes is introduced. Poisson intensity estimation is a vital task in various applications including medical imaging, astrophysics and network traffic analysis. The new approach enables full posterior inference of the intensity in a non-parametric regression setting. The performance of the novel scheme is demonstrated through simulation studies on synthetic and real datasets up to five dimensions, and the new scheme is compared with alternative approaches.
