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BEGIN:VEVENT
DTSTAMP:20260427T062520Z
DTSTART;VALUE=DATE-TIME:20251201T130000
DTEND;VALUE=DATE-TIME:20251201T140000
SUMMARY:WCPM: Dennis Prangle\, University of Bristol
TZID:Europe/London
UID:20251201-8ac672c498efcc010198f0925a67047b@warwick.ac.uk
CREATED:20250908T115349Z
DESCRIPTION:Location: Lecture Theatre 0.04 IMC Networking Lunch: The Rech
 arge Room\, next to Lecture Theatre 004\, from 12:30pm - 1pm. Title: Inf
 erence for State Space Models with Long Data by Variational Methods Abst
 ract: Statistical and machine learning methods allow practical inference
  for complex state space models (SSMs). However\, standard approaches re
 quire sampling hidden states for the entire time range of interest in ea
 ch training iteration\, which can be impractically costly for larger dat
 asets. Methods which use a shorter minibatch of data in each iteration a
 re cheaper\, but can introduce severe bias in a time series context. We 
 avoid this by adapting the buffering approach of Aicher et al. (2019\, 2
 025) to a variational inference context. We provide theoretical results 
 supporting our approach\, and empirical studies showing that the method 
 achieves accurate results and orders of magnitude speed-ups. Bio: Dennis
  Prangle is an associate professor in statistics at the University of Br
 istol. His current research is on the interface between Bayesian statist
 ics and machine learning. He is particularly interested in developing ap
 proximate inference methods such as simulation based inference approache
 s\, variational inference and composite likelihood. One application is t
 o likelihood-free inference\, where simulation of data is possible but t
 he likelihood function is unavailable. Another is to stochastic differen
 tial equations and he has worked on applications to population genetics\
 , physics\, ecology and epidemiology. He is also interested in experimen
 tal design and how to quickly derive effective high dimensional designs.
 
LOCATION:Lecture Theatre 0.04 IMC
CATEGORIES:WCPM
LAST-MODIFIED:20250908T115349Z
ORGANIZER;CN=Jin Kang:
END:VEVENT
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