Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows
Eleonora Villa, Golam Mohiuddin Shaifullah, Andrea Possenti, Carmelita Carbone
我们详细介绍了贝叶斯脉冲星定时阵列数据的推理工作流程,重点是通过使用基于流的嵌套采样规范化来提高效率,稳健性和速度。 在 Enterprise 框架的基础上,我们集成了 i-nessai 采样器,并将其性能用于逼真的模拟数据集。 我们分析其计算扩展和稳定性,并表明它实现了准确的后验和可靠的证据估计,并大大减少了运行时,根据数据集配置,相对于传统的单核并行脾温MCMCM分析,最多三个数量级。 这些结果突出了基于流量的嵌套采样在保持推理质量的同时加速PTA分析的潜力。
We present a detailed study of Bayesian inference workflows for pulsar timing array data with a focus on enhancing efficiency, robustness and speed through the use of normalizing flow-based nested sampling. Building on the Enterprise framework, we integrate the i-nessai sampler and benchmark its performance on realistic, simulated datasets. We analyze its computational scaling and stability, and show that it achieves accurate posteriors and reliable evidence estimates with substantially reduced ...