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SUMMARY:Exploiting Symmetries to Construct Efficient MCMC Algorithms With
an Application to SLAM - Prof Csaba Szepesvari (Department of Computing Sc
ience\, University of Alberta)
DTSTART;VALUE=DATE-TIME:20151111T130000Z
DTEND;VALUE=DATE-TIME:20151111T140000Z
UID:https://talks.ox.ac.uk/talks/id/eb866ef8-ff0c-4467-85dd-0809c58ae39c/
DESCRIPTION:Sampling from a given target distribution in an efficient mann
er is a widely studied problem with applications in computer science\, ope
rations research\, statistics\, and many applied subjects. Due to its gene
rality and flexibility\, one of the most successful approach to design eff
icient sampling methods uses the so-called Monte Carlo Markov Chain techni
que\, is the Metropolis-Hastings (MH) algorithm\, which\, in one venue was
named as one of the "top 10" algorithms in computer science. In this talk
we will explore how group moves can be added to MH in general state space
s\, broadening further the applicability of MH beyond what is available to
day. The main motivation for adding group moves to MH is because they allo
w a convenient way to exploit invariances in the target distribution\, eve
n if those only concern a subset of the factors\, or even if they are only
approximate. The main technical difficulty in applying MH in this setting
is the computation of the acceptance probability\, which we address with
tools of topological group theory based on a general result of Luke Tierne
y. The method is demonstrated in an application to robotics in the so-call
ed simultaneous localization and mapping (SLAM) problem where a robot navi
gating a 2D environment and equipped with range only sensors has to learn
a map of its environment while simultaneously estimating its positions. Ou
r experiments with real-world benchmark data on this problem shows that ou
r general method performs competitively with special-purpose algorithms.\n
\nThis is joint work with Roshan Shariff and Andras Gyorgy. The talk is ba
sed on this paper\, that appeared at AISTAT 2015.\n\nSpeakers:\nProf Csaba
Szepesvari (Department of Computing Science\, University of Alberta)
LOCATION:1 South Parks Road (Lecture Theatre\, Department of Statistics)\,
1 South Parks Road OX1 3TG
URL:https://talks.ox.ac.uk/talks/id/eb866ef8-ff0c-4467-85dd-0809c58ae39c/
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DESCRIPTION:Talk:Exploiting Symmetries to Construct Efficient MCMC Algorit
hms With an Application to SLAM - Prof Csaba Szepesvari (Department of Com
puting Science\, University of Alberta)
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