BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:A linear PDF model for robust Bayesian inference - Mark Costantini
  (DAMTP)
DTSTART:20250314T160000Z
DTEND:20250314T170000Z
UID:TALK228712@talks.cam.ac.uk
CONTACT:Nico Gubernari
DESCRIPTION:Accurate uncertainty propagation is crucial for parton distrib
 ution functions (PDFs)\, particularly given the high-precision data expect
 ed from the HL-LHC. Traditional non-Bayesian approaches often struggle wit
 h strong non-linear dependencies in the forward map\, motivating the need 
 for more reliable Bayesian inference methods. However\, these methods come
  with significant computational costs.\nAn ideal PDF parametrisation shoul
 d satisfy three key criteria: (i) it must respect theoretical constraints\
 , such as small- and large-x scaling behaviour\, sum rules\, and integrabi
 lity\; (ii) it should be sufficiently flexible to explore the space of can
 didate PDFs within the set of continuous\, differentiable functions\; and 
 (iii) it should allow for efficient fitting of model parameters. While muc
 h attention has been given to the first two properties\, the third—exped
 ience of fitting—has remained largely unoptimised in the literature.\nTh
 e goal of this talk is to explore this third aspect\, focusing on strategi
 es to improve the efficiency of PDF fitting.
LOCATION:MR19 (Potter Room\, Pavilion B)\, CMS
END:VEVENT
END:VCALENDAR