BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:1
UID:UW-Physics-Event-8135
DTSTART:20230208T170000Z
DTEND:20230208T181500Z
DTSTAMP:20260408T025109Z
LAST-MODIFIED:20230206T210257Z
LOCATION:Online Seminar: Please sign up for our mailing list at www.ph
 ysicsmeetsml.org for zoom link
SUMMARY:Bayesian Renormalization: An explicit correspondence between s
 tatistical inference and exact renormalization\, Physics ∩ ML Semina
 r\, Marc Klinger\, University of Illinois Urbana-Champaign
DESCRIPTION:Renormalization is a ubiquitous tool in theoretical physic
 s used to understand the role of scale in organizing natural phenomena
 . In this presentation we will report on a new information theoretic p
 erspective for understanding the Exact Renormalization Group (ERG) thr
 ough the intermediary of Bayesian Statistical Inference. This connecti
 on is facilitated by the Dynamical Bayesian Inference scheme\, which e
 ncodes Bayesian inference in the form of a one parameter family of pro
 bability distributions solving an integro-differential equation derive
 d from Bayes’ law. Utilizing the picture of an ERG flow as a functio
 nal diffusion process\, we arrive at a dictionary outlining how renorm
 alization can be understood as an inverse process relative to a Dynami
 cal Bayesian inference scheme. A particularly salient feature of this 
 correspondence is that it identifies the role of Fisher geometry in pr
 oviding an emergent scale for “Bayesian Renormalization” that is r
 elated to the precision with which nearby points in model space can be
  differentiated. We comment on the usefulness of this identification i
 n data science applications including possible implementations of “B
 ayesian Renormalization” as a tool for refining diffusion learning t
 echniques.
URL:https://www.physics.wisc.edu/events/?id=8135
END:VEVENT
END:VCALENDAR