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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Computationally Efficient Algorithms for Detecting
  Changepoints - Fearnhead\, P (Lancaster Universit
 y)
DTSTART;TZID=Europe/London:20140116T093000
DTEND;TZID=Europe/London:20140116T100000
UID:TALK49978AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/49978
DESCRIPTION:We consider algorithms that can obtained the optim
 al segmentation of data under approaches such as p
 enalised likelihood. The penalised likelihood crit
 eria requires the user to specify a penalty value\
 , and the choice of penalty will affect the number
  of changepoints that are detected. We show how it
  is possible to obtain the optimal segmentation fo
 r all penalty values across a continuous range. Th
 e computational complexity of this approach can li
 near in the number of data points\, and linear in 
 the difference in the number of changepoints betwe
 en the optimal segmentations for the smallest and 
 largest penalty values. The algorithm can be used 
 to find optimal segmentations under the minimum de
 scription length criteria in a much more efficient
  manner than using the segment neighbourhood algor
 ithm.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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