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UID:event-7370@www.amse-aixmarseille.fr
DTSTAMP:20260317T091945Z
CREATED:20260317T091945Z
LAST-MODIFIED:20260317T091945Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Nicolas Chopin
DTSTART:20210126T130000Z
DTEND:20210126T143000Z
DESCRIPTION:SMC (Sequential Monte Carlo) samplers present clear several adv
 antages over MCMC (Markov chain Monte Carlo). In particular\, they require 
 little tuning (or more precisely\, it is easy to automate their tuning to a
  given problem)\; they are easy to parallelize\; and they allow for estimat
 ing the marginal likelihood of the target distribution. In this talk\, I wi
 ll discuss how SMC may be used in various problems in machine learning and 
 computational statistics\, and why they remain slightly overlooked in these
  areas. One possible reason (among several others) is that the following di
 fficulty with SMC samplers may have been overlooked in the literature: that
 \, to obtain optimal performance\, one may need to apply a large number of 
 MCMC steps at each iteration.I will also present a recent paper (joint work
  with Hai-Dang Dau) where we develop a new type of SMC sampler\, where all 
 the intermediate Markov steps are used as "particles". That makes the resul
 ting algorithm typically more efficient\, and more importantly much more ro
 bust to user choices\, and thus ultimately easier to use.\\n\\nContact: Mic
 hel Lubrano: michel.lubrano[at]univ-amu.frPierre Michel: pierre.michel[at]
 univ-amu.fr\n\nPlus d'informations: https://www.amse-aixmarseille.fr/en/eve
 nts/nicolas-chopin-0
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://www.amse-aixmarseille.fr/en/events/nicolas-chopin-0
CONTACT:Michel Lubrano: michel.lubrano[at]univ-amu.frPierre Michel:&nbsp\;p
 ierre.michel[at]univ-amu.fr
TRANSP:OPAQUE
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