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SUMMARY:Computationally Efficient Algorithms for Detecting Changepoints - 
 Fearnhead\, P (Lancaster University)
DTSTART:20140116T093000Z
DTEND:20140116T100000Z
UID:TALK49978@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:We consider algorithms that can obtained the optimal segmentat
 ion of data under approaches such as penalised likelihood. The penalised l
 ikelihood criteria 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 for all pe
 nalty values across a continuous range. The computational complexity of th
 is approach can linear in the number of data points\, and linear in the di
 fference in the number of changepoints between the optimal segmentations f
 or the smallest and largest penalty values. The algorithm can be used to f
 ind optimal segmentations under the minimum description length criteria in
  a much more efficient manner than using the segment neighbourhood algorit
 hm.\n
LOCATION:Seminar Room 1\, Newton Institute
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