发表机构
Indian Institute of Science Education and Research Thiruvananthapuram（印度科学教育研究所特里凡得琅分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种以数据大小为退火参数的顺序集成MCMC方法，用于贝叶斯脉冲星计时与噪声分析，通过渐进增加数据量实现可靠收敛并降低计算成本，并在NANOGrav 12.5年数据上验证了其有效性。
AI 中文摘要
我们开发了一种用于贝叶斯脉冲星计时与噪声分析的顺序集成马尔可夫链蒙特卡罗（MCMC）方案，该方案以数据大小作为退火参数。通过迭代增加数据大小，该方法能够从可能无信息的初始点逐步且可靠地收敛，从而增加灵敏度并逐步缩小目标分布，直至达到完整数据集的后验分布。初始迭代使用比完整数据集小数个数量级的子数据集，大幅降低了计算成本，并通过采用由这些子数据集降低灵敏度所证明的近似低秩模型进一步降低了成本。贝叶斯推断使用从真实目标分布中采样得到的最终MCMC链，并经过适当的烧入期处理。我们使用NANOGrav 12.5年数据中的PSR B1855+09演示了该方法，展示了向目标后验的渐进收敛，并且大部分计算时间用于采样真实目标分布，而非之前的退火迭代。
英文摘要
We develop a sequential ensemble MCMC scheme for Bayesian pulsar timing and noise analysis that uses data size as the tempering parameter. The method enables gradual and reliable convergence from potentially uninformed initial points by iteratively increasing the data size, thereby increasing sensitivity and progressively narrowing the target distribution until the posterior of the full dataset is reached. The initial iterations use sub-datasets that can be orders of magnitude smaller than the full dataset, substantially reducing computational cost, which is further lowered by employing approximate lower-rank models justified by the reduced sensitivity of these sub-datasets. Bayesian inference is performed using the final MCMC chain sampled from the true target distribution after applying appropriate burn-in. We demonstrate the method using the NANOGrav 12.5-year data of PSR B1855+09, showing progressive convergence to the target posterior and that most of the computational time is spent sampling the true target distribution rather than in the preceding tempered iterations.
CommentsSubmitted to Physical Review D
