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BEGIN:VEVENT
DTSTAMP:20261006T221711Z
DTSTART;VALUE=DATE-TIME:20251027T130000
DTEND;VALUE=DATE-TIME:20251027T140000
SUMMARY:WCPM: Nick Tawn\, Warwick University
TZID:Europe/London
UID:20251027-8ac672c598f027f20198f070772d13ba@warwick.ac.uk
CREATED:20251021T090856Z
DESCRIPTION:Location: Lecture Theatre 0.04 IMC Networking Lunch: The Rech
 arge Room\, next to Lecture Theatre 004\, from 12:30pm - 1pm. Title: Ann
 ealed Leap-Point Sampler for Multimodal Target Distributions Abstract: I
 n Bayesian statistics\, exploring high-dimensional multimodal posterior 
 distributions poses major challenges for existing MCMC approaches. This 
 paper introduces the Annealed Leap-Point Sampler (ALPS)\, which augments
  the target distribution state space with modified annealed (cooled) dis
 tributions\, in contrast to traditional tempering approaches. The coldes
 t state is chosen such that its annealed density is well-approximated lo
 cally by a Laplace approximation. This allows for automated setup of a s
 calable mode-leaping independence sampler. ALPS requires an exploration 
 component to search for the mode locations\, which can either be run ada
 ptively in parallel to improve these mode-jumping proposals\, or else as
  a pre-computation step. A theoretical analysis shows that for a d-dimen
 sional problem the coolest temperature level required only needs to be l
 inear in dimension\, O (d)\, implying that the number of iterations need
 ed for ALPS to converge is O (d) (typically leading to overall complexit
 y O(d^3) when computational cost per iteration is taken into account). A
 LPS is illustrated on several complex\, multimodal distributions that ar
 ise from real-world applications. This includes a seemingly-unrelated re
 gression (SUR) model of longitudinal data from U.S. manufacturing firms\
 , as well as a spectral density model that is used in analytical chemist
 ry for identification of molecular biomarkers. Bio: Dr Nick Tawn is a Re
 ader in Statistics at the University of Warwick. His research interests 
 have mostly focussed on developing scalable Monte Carlo methodology for 
 use in complex Bayesian settings\; in particular MCMC and SMC techniques
 . He also takes a keen interest in the Machine Learning/Data Science lit
 erature with a view to using these methods to complement and accelerate 
 the inference process. More specifically his research has focussed on MC
 MC methodology in settings where the target distribution exhibits multi-
 modality. My PhD thesis\, completed in 2017\, was titled "Towards Optima
 lity of the Parallel Tempering Algorithm". Since completion of his thesi
 s he has continued to work on similar problems whilst writing up the ide
 as from my thesis for publication.
LOCATION:Lecture Theatre 0.04 IMC
CATEGORIES:WCPM
LAST-MODIFIED:20251021T090856Z
ORGANIZER;CN=Jin Kang:
END:VEVENT
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