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SUMMARY:Introduction to Bayesian inference - Thomas Brouwer (University of
  Cambridge)
DTSTART:20151117T130000Z
DTEND:20151117T140000Z
UID:TALK60536@talks.cam.ac.uk
CONTACT:Heidi Howard
DESCRIPTION:Probabilistic models allow us to make flexible and robust syst
 ems that handle uncertainty in our data gracefully. In a Bayesian approach
  we express prior beliefs over our model's parameters\, and update our bel
 iefs by finding the posterior distribution over the parameters.\nIn this l
 ecture\, we will consider how these models can be described graphically\, 
 and how efficient Bayesian inference can be used for training.
LOCATION:Computer Laboratory\, William Gates Building\, Room SW01
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