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SUMMARY:Detecting change-points in the structure of a network: Exact Bayes
 ian inference - Stéphane Robin (INRA - Institut National de la Recherche 
 Agronomique)
DTSTART:20161213T160000Z
DTEND:20161213T164500Z
UID:TALK69521@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Joint work with Lo&iuml\;c Schwaller <br><br>We consider the p
 roblem of change-point detection in multivariate time-series\, typically t
 he expression of a set of genes\, or the activity of a set of brain region
 s over time. We adopt the framework of graphical models to described the d
 ependency between the series. We are interested in the situation where the
  graphical model is affected by abrupt changes throughout time. In the abo
 ve examples\, such changes correspond to gene or brain region rewiring.  <
 br><span><br> We demonstrate that it is possible to perform exact Bayesian
  inference whenever one considers a simple class of undirected graphs call
 ed spanning trees as possible structures. We are then able to integrate on
  both the graph and segmentation spaces at the same time by combining clas
 sical dynamic programming with algebraic results pertaining to spanning tr
 ees. In particular\, we show that quantities such as posterior distributio
 ns for change-points or posterior edge probabilities over time can efficie
 ntly be obtained. <br></span>  <span><br> We illustrate our results on bot
 h synthetic and experimental data arising from molecular biology and neuro
 science.&nbsp\;</span>
LOCATION:Seminar Room 1\, Newton Institute
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