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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Gaussian mixture transition models for identificat
ion of slow processes in molecular kinetics - Wu\,
H (Freie Universitt Berlin)
DTSTART;TZID=Europe/London:20140318T151500
DTEND;TZID=Europe/London:20140318T155500
UID:TALK51485AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/51485
DESCRIPTION:The identification of slow processes from molecula
r dynamics (MD) simulations is a fundamental and i
mportant problem for analyzing and understanding c
omplex molecular processes\, because the slow proc
esses governed by dominant eigenvalues and eigenfu
nctions of MD propagators contain essential inform
ation on structures and transition rates of metast
able conformations. Most of the existing approache
s to this problem\, including Markov model based a
pproaches and the variational approach\, perform t
he identification by representing the dominant eig
enfunctions as linear combinations of a set of bas
is functions. But the choice of basis functions is
still an unsatisfactorily solved problem for thes
e approaches. Here we take a Bayesian approach to
slow process identification by developing a novel
parametric model called Gaussian mixture transitio
n model (GMTM) to characterize MD propagators. The
GMTM approximates the half-weighted density of a
MD propagator by a Gaussian mixtur e model and all
ows for tractable computation of spectral componen
ts. In contrast with the other Galerkin-type appro
ximation based approaches\, our approach can autom
atically adjust the involved Gaussian basis functi
ons and handle the statistical uncertainties in th
e Bayesian framework. We demonstrate by some simul
ation examples the effectiveness and accuracy of t
he proposed approach.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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