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SUMMARY:Inferring change points in signal levels through deterministic min
 imization of a generalized global functional - Little\, M (Aston Universit
 y/MIT)
DTSTART:20140116T155000Z
DTEND:20140116T161000Z
UID:TALK49980@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Abrupt level change points are ubiquitous. Knowing the change 
 points and levels of a time series\, is critical to many practical signal 
 analysis problems in science and engineering. For this\, and other reasons
 \, the problem of detecting level shifts\, first studied in the 1940's in 
 process control\, is of enduring interest. In this talk I will detail a se
 t of simple\, novel\, generalized\, deterministic nonlinear algorithms for
  this problem. These algorithms are based on a global functional which\, w
 hen minimized\, finds the maximum a-posteriori location of the change poin
 ts and values of the levels. This global functional approach subsumes some
  well-known algorithms for this problem that have been developed in digita
 l image processing contexts\, and also folds in several algorithms from st
 atistical machine learning that have hitherto been seen as distinct. The a
 lgorithms are computationally simple\, and many are convex optimization pr
 oblems for which standard\, fast implementations are available.\n
LOCATION:Seminar Room 1\, Newton Institute
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