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SUMMARY:An Introduction to Simple Markov Models - Richard Turner Microsoft
  Research Ltd
DTSTART:20190220T100000Z
DTEND:20190220T110000Z
UID:TALK120526@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Sequence data can be found everywhere: from base pairs in a DN
 A molecule to characters in handwritten text. We need models for sequences
  to predict or forecast future data\, to remove noise\, and to aid underst
 anding (e.g. by identifying underlying latent variables). Markov models ar
 e the simplest such models. They can be used in their own right\, or compo
 sed to form more complex models. In this tutorial I will give a very short
  introduction to Markov models for discrete valued data (N-gram models) an
 d real-valued data (auto-regressive models). The goal is to give an intuit
 ive feel for these models\, rather than an exhaustive exposition. If time 
 permits\, I will show how they can be composed with more complex component
 s\, such as neural networks\, to perform complex tasks. 
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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