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SUMMARY:Gaussian mixture transition models for identification of slow proc
 esses in molecular kinetics - Wu\, H (Freie Universitt Berlin)
DTSTART:20140318T151500Z
DTEND:20140318T155500Z
UID:TALK51485@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The identification of slow processes from molecular dynamics (
 MD) simulations is a fundamental and important problem for analyzing and u
 nderstanding complex molecular processes\, because the slow processes gove
 rned by dominant eigenvalues and eigenfunctions of MD propagators contain 
 essential information on structures and transition rates of metastable con
 formations. Most of the existing approaches to this problem\, including Ma
 rkov model based approaches and the variational approach\, perform the ide
 ntification by representing the dominant eigenfunctions as linear combinat
 ions of a set of basis functions. But the choice of basis functions is sti
 ll an unsatisfactorily solved problem for these approaches. Here we take a
  Bayesian approach to slow process identification by developing a novel pa
 rametric model called Gaussian mixture transition model (GMTM) to characte
 rize MD propagators. The GMTM approximates the half-weighted density of a 
 MD propagator by a Gaussian mixtur e model and allows for tractable comput
 ation of spectral components. In contrast with the other Galerkin-type app
 roximation based approaches\, our approach can automatically adjust the in
 volved Gaussian basis functions and handle the statistical uncertainties i
 n the Bayesian framework. We demonstrate by some simulation examples the e
 ffectiveness and accuracy of the proposed approach.\n
LOCATION:Seminar Room 1\, Newton Institute
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