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SUMMARY:Robust and scalable inference for simulator-based models
DTSTART:20230317T151500
DTEND:20230317T170000
DTSTAMP:20260406T202232Z
UID:1ad07b9156b7512031b6b9782152f404bcf0e36c8772ab8d9a70b22c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Jukka Corander\, Wellcome Sanger Institute\nSimulator-based mo
 dels are becoming increasingly popular in many research\ndomains across ac
 ademia and industry. Calibration of such models in the light of\ndata and 
 quantification of uncertainty about model parameters are key challenges\nf
 or practical applications and the topic has received increasing attention\
 nduring the past decade. We will discuss various inference techniques for\
 nsimulator-based models that improve computational feasibility by adopting
 \ntechniques from machine learning to build surrogate models for the appro
 ximate\nlikelihood or posterior. Asymptotic properties of Jensen-Shannon D
 ivergence based inference will also be briefly\nconsidered. Several of the
  discussed approaches are available in the\nopen-source software platform 
 Engine for Likelihood-Free Inference (ELFI): http://elfi.ai.\n 
LOCATION:GA 3 21 https://plan.epfl.ch/?room==GA%203%2021
STATUS:CANCELLED
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