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SUMMARY:Filip Tronarp (Lund University)
DTSTART:20251119T121500Z
DTEND:20251119T130000Z
DTSTAMP:20260422T155318Z
UID:gbgstats/94
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/94/
 ">A Recursive Theory of Variational State Estimation: The Dynamic Programm
 ing Approach</a>\nby Filip Tronarp (Lund University) as part of Gothenburg
  statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nIn this talk\, 
 we discuss the variational inference problem in partially observed Markov 
 processes from the dynamic programming perspective. \nThis leads to a back
 ward and a forward recursion for certain value functionals\, which are clo
 sely connected to the corresponding recursions from classical Bayesian sta
 te estimation theory. Namely\, the backward value functional is a lower bo
 und on the "backward filter" and the forward value functional is a lower b
 ound on the unnormalized filtering density. The two recursions can also be
  combined yielding a variational two-filter formula.\nWhat results is a va
 riational state estimation theory that is completely analogous to the clas
 sical Bayesian state estimation theory. \nThe theory is applied to a jump 
 Gauss-Markov regression problem\, where closed form solutions to the value
  functional recursions can be obtained.\n
LOCATION:https://researchseminars.org/talk/gbgstats/94/
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