This paper presents an extraction of unpolarized quark transverse-momentum-dependent parton distribution functions (TMD PDFs) from Drell-Yan data using an AI-assisted Bayesian inference framework. It performs a global analysis of Drell-Yan data and compares the results with those obtained using the replica method, highlighting differences in uncertainty estimates.

This paper introduces Adjusted Pairwise Likelihood (APW) methods for Bayesian node-age estimation, offering computationally efficient and calibration-robust alternatives to traditional full phylogenetic likelihood approaches. The APW methods demonstrate comparable accuracy to full likelihood while showing increased resilience to errors in fossil calibration and prior specification, making them well-suited for large phylogenomic datasets.