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SUMMARY:#StudentSeminar: Sampling from complex probability distributions:
from Monte Carlo methods to machine-learning normalizing flows
DTSTART;VALUE=DATE-TIME:20240905T130000Z
DTEND;VALUE=DATE-TIME:20240905T140000Z
DTSTAMP;VALUE=DATE-TIME:20240910T152641Z
UID:indico-event-7722@indico.ific.uv.es
DESCRIPTION:Abstract: \nBeing able to obtain samples from a probability di
stribution is key in many scientific applications\, such as statistical in
ference and the simulation of statistical systems. In particular\, Quantum
Chromodynamics (QCD) in discretized spacetime can be treated as a statist
ical system\, and its study boils down to obtaining samples from a very co
mplex and high-dimensional probability distribution. In this talk I will t
ry to do a friendly exploration of the foundations of sampling methods\, i
ntroducing Monte Carlo techniques such as importance sampling and Markov C
hain Monte Carlo (MCMC) algorithms. This will pave the way to talk about a
modern machine learning sampling technique known as normalizing flows\, a
nd we will discuss its possible applications for lattice QCD simulations.\
n\n \n\n(Theoretical physics)\n\nhttps://indico.ific.uv.es/event/7722/
LOCATION:Universe 1001-Primera-1-1-1 - Paterna. Seminario
URL:https://indico.ific.uv.es/event/7722/
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