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CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20250317T184046Z
DTSTART:20250319T140000Z
DTEND:20250319T150000Z
SUMMARY:Statistics Seminar - Prof. Victor Elvira (Univ. of Edinburgh)
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}b1gh-m67u7t
 mb-fyhys7
DESCRIPTION:State-space Models as Graphs\n\nProf. Victor Elvira \nSchool 
 of Mathematics\,  University of Edinburgh\n\nAbstract: Modelling and inf
 erence in multivariate time series is central in statistics\, signal pro
 cessing\, and machine learning. A fundamental question when analysing mu
 ltivariate sequences is the search for relationships between their entri
 es (or the modelled hidden states)\, especially when the inherent struct
 ure is a directed (causal) graph. In such context\, graphical modelling 
 combined with sparsity constraints allows to limit the proliferation of 
 parameters and enables a compact data representation which is easier to 
 interpret in applications\, e.g.\, in inferring causal relationships of 
 physical processes in a Granger sense. In this talk\, we present a novel
  perspective consisting on state-space models being interpreted as graph
 s. Then\, we propose novel algorithms that exploit this new perspective 
 for the estimation of the linear matrix operator and also the covariance
  matrix in the state equation of a linear-Gaussian state-space model. Fi
 nally\, we discuss the extension of this perspective for the estimation 
 of other model parameters in more complicated models.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Frank Adams 2\, Alan Turing Building\, Manchester
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