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SUMMARY:Introduction to Bayesian Statistical Learning
DTSTART:20260316T120000Z
DTEND:20260320T160000Z
DTSTAMP:20260424T091600Z
UID:indico-event-222@events.hpc-portal.eu
CONTACT:ai-courses-jsc@fz-juelich.de
DESCRIPTION:When observing data\, the key question is: What I can learn fr
 om the observation? Bayesian inference treats all parameters of the model 
 as random variables. The main task is to update their distribution as new 
 data is observed. Hence\, quantifying uncertainty of the parameter estimat
 ion is always part of the task. In this course we will introduce the basic
  theoretical concepts of Bayesian Statistics and Bayesian inference. We di
 scuss the computational techniques and their implementations\, different t
 ypes of models as well as model selection procedures. We work with existin
 g datasets and use the PyMC3 framework for practicals.\nThe main topics ar
 e:\n\nBayes theorem\nPrior and posterior distributions\nComputational chal
 lenges and techniques: MCMC\, variational approaches\nModels: Mixture Mode
 ls\, Bayesian Neural Networks\, Variational Autoencoder\, Normalising Flow
 s\nPyMC3 framework for Bayesian computation\nRunning Bayesian models on a 
 supercomputer\n\n \n\n\n\n\nContents level\n\n\nin hours\n\n\nin %\n\n\n\
 n\n\n\nBeginner's contents:\n\n\n4.5 h\n\n\n30 %\n\n\n\n\nIntermediate con
 tents:\n\n\n10.5 h\n\n\n70 %\n\n\n\n\nAdvanced contents:\n\n\n0 h\n\n\n0 %
 \n\n\n\n\nCommunity-targeted contents:\n\n\n0 h\n\n\n0 %\n\n\n\n\n \nPrer
 equisites:\nParticipants should be familiar with general statistical conce
 pts\, such as distributions\, samples. Furthermore\, familiarity with fund
 amental Machine Learning concepts such as regression\, classification and 
 training is helpful.\nA personal institutional email address (university/r
 esearch institution\, government agency\, organisation\, or company) is re
 quired to register for JSC training courses. If you don't have an institut
 ional email address\, please get in touch with the contact person for this
  course.\nTarget Audience:\nPhD students and Postdocs\nLearning Outcome:\n
 The ability to set up a Bayesian approach within a given framework\nLangua
 ge:\nThis course is given in English.\nDuration:\n5 half days\nDates:\n16-
 20 March 2026\, 13:00 - 17:00\nVenue:\nOnline\nNumber of Participants:\nMa
 ximum 25\nInstructor:\nAlina Bazarova\, Jose Robledo (JSC)\n\nhttps://even
 ts.hpc-portal.eu/event/222/
LOCATION:Online
URL:https://events.hpc-portal.eu/event/222/
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