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SUMMARY:Information-theoretic techniques and context-tree methods for time
  series - Ioannis Papageorgiou\, University of Cambridge
DTSTART:20240221T140000Z
DTEND:20240221T150000Z
UID:TALK209224@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:Building on the context-tree weighting (CTW) circle of ideas\,
  we introduce a collection of statistical ideas and algorithmic tools for 
 modelling and performing exact inference with both discrete and real-value
 d time series. For discrete time series\, we describe a novel Bayesian fra
 mework based on variable-memory Markov chains\, called Bayesian Context Tr
 ees (BCT). A general prior structure is introduced\, and a collection of m
 ethodological and algorithmic tools is developed\, allowing for efficient\
 , exact Bayesian inference. The proposed approach is then extended to real
 -valued time series\, where it is employed to develop a general hierarchic
 al Bayesian framework for building mixture models based on context trees. 
 Again\, effective computational tools are developed\, allowing for efficie
 nt\, exact Bayesian inference. The proposed methods are found to outperfor
 m several state-of-the-art techniques on both simulated and real-world dat
 a from a wide range of applications. This is joint work with Ioannis Konto
 yiannis.  
LOCATION:MR5\, CMS Pavilion A
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